Occupant Aware, Intelligent and Adaptive Enterprises Adapt4EE, Grant Agreement No. 288150

Adapt4EE Deliverable D1.3 Dissemination Level (PU) Grant Agreement No. 288150

April 2012 1 CERTH

SEVENTH FRAMEWORK PROGRAMME

ICT systems for Energy Efficiency

Project Title:

Occupant Aware, Intelligent and Adaptive Enterprises

Adapt4EE, Grant Agreement No. 288150

Deliverable

State-of-Art and Industry Analysis Report

Deliverable No. D1.3

Workpackage No.

WP1 Workpackage Title and task type

Adapt4EE Definition

RTD

Task No. T1.3 Task Title SoA Analysis (Technologies, Tools and Respective Projects)

Lead beneficiary CERTH

Dissemination level PU - All

Nature of Deliverable R

Delivery date April 2012

Status (F: final; D: draft; RD: revised draft):

F

File Name: Adapt4EE-Deliverable-D1.3.doc

Project start date and duration 01 November 2011, 36 Months


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Authors List

Leading Author (Editor)

Surname Initials Beneficiary Name Contact email

Tzovaras D CERTH [email protected]

Co-authors (in alphabetic order)

# Surname Initials Beneficiary Name Contact email

1 Brennan T BOC [email protected]

2 Eguaras M UNAV [email protected]

3 Elmasllari E FhG/FIT [email protected]

4 Ferreira R ISA [email protected]

5 Hreno J TUK [email protected]

6 Kamadanis N CERTH [email protected]

7 Katsaitis D CERTH [email protected]

8 Malavazos C HYPERTECH [email protected]

9 Serban R ALMENDE [email protected]

Reviewers List

List of Reviewers (in alphabetic order)

# Surname Initials Beneficiary Name Contact email

1 Rodrigues P AAC [email protected]

2 Simoes P ISA [email protected]

3 Vidaurre M UNAV [email protected]


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Document history

Version Date Status Modifications made by

1.0 January 2012 Circulation of deliverable template for comments by involved partners

CERTH

2.0 February 2012 Draft version of the table of contents based on the revised version of the deliverable

CERTH

3.0 March 2012 Revised version available with contribution from partners

CERTH, ALL

4.0 April 2012 Complete version including comments from the peer review process

CERTH


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List of definitions & abbreviations

Abbreviation Definition

A&E Architecture & Engineer

AADCC Accuracy and Ability to simulate Complex and Detailed

building Components

ABMS Agent-based Modelling and Simulation

AEC Architecture, Engineering and Construction

AGC Associated General Contractors

API Application Programming Interface

ASHRAE American Society of Heating, Refrigeration, and Air-Conditioning Engineers

BER Building Energy Rate

BESD Building Energy Software Tools Directory

BI Business Intelligence

BIM Building Information Modelling

BPM Business Process Modelling

BPMN Business Process Modelling Notation

BPMS Business Process Management System

BPS Building Performance Simulation

CASE Council of American Structural Engineers

CFD Computational Fluid Dynamics

CIS/2 CIMsteel Integration Standard 2

CIBSE Chartered Institution of Building Services Engineers

COBIE Construction to Operations Building Information Exchange

CSG Constructive Solid Geometry

D&Es Designers & Engineers

DOE Department of Energy

DOLCE Descriptive Ontology for Linguistic and Cognitive Engineering

DPWS Device Profile for Web Services

DXF Drawing Interchange Format

ES Embedded System


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FIPA Foundation for Intelligent Physical Agent

GBS Green Building Studio

gbXML Green Building XML schema

IBDP Integrated Building Design Process

IBM Interoperability of Building Modelling

IDE Integrated Development Environment

IFC Industry Foundation Class

IFD International Framework for Dictionaries

IGES Initial Graphic Exchange Specification

IIKB Integration of Intelligent design Knowledge-Base

IIOP Internet Inter-ORB Protocol

IoT Internet of Things

IPD Integrated Project Delivery

ISO International Organization for Standardization

ISOSTEP International Standards Organization-Standard for the Exchange of Product Model Data

JMS Java Message Service

KPI Key Performance Indicators

LEED Leadership in Energy and Environmental Design (rating system; US Green Building Council)

LLC Limited Liability Corporations

MAS Multi-Agent System

NREL National Renewable Energy Laboratory

NZEB Nearly Zero-Energy Buildings (nZEB)

OMG Object Management Group

OO Object Oriented

OSGi Open Services Gateway Initiative

OWL Web Ontology Language (W3C)

RDF Resource Description Framework

RFID Radio Frequency Identification

RMI Remote Method Invocation

ROI Return on Investment

SAIL Storage And Inference Layer


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SEI Structural Engineering Institute

SI Smart Items Services Infrastructure

SLP Service Location Protocol

SoA State-of-the-art

SOA Service Oriented Architecture

SOAP Simple Object Access Protocol (XML protocol)

SSDP Simple Service Discovery Protocol

SSN Semantic Sensor Network

STEP Standard for the Exchange of Product Model Data (ISO 10303)

SWRL Semantic Web Rule Language

UI User Interface

UIM Usability and Information Management

UML Unified Modeling Language

UPnP Universal Plug and Play

VDC Virtual Design & Construction

VM Virtual Machine

VMB Virtual Building Modelling

WS Web Service

WSN Wireless Sensor Network2

XML Extensible Markup Language


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Executive Summary

This deliverable reports the results of the literature review and market survey

carried out in the framework of the European Union (EU) 7th Framework

Programme (FP7) Specific Targeted Research Project (STREP) “Occupant Aware,

Intelligent and Adaptive Enterprises (Adapt4EE)” including state-of-the-art

technologies (SoA), tools and respective projects in the areas related to the scope

of Adapt4EE. The main findings of this survey have been carried out within the

activities performed in Task T1.3 “SoA Analysis (Technologies, Tools and

Respective Projects)” of Adapt4EE WP1 “Adapt4EE Definition”.

The document focuses mainly on topics that are highly related and applicable to

Adapt4EE such as building performance tools, past and recent research on

occupancy modelling, semantic technologies used for analyzing building

performance in close correlation with the enterprises to be “housed” in the

buildings under design as well as technologies used by researchers and the

Architecture, Engineering and Construction (AEC) technology vendors for

visualization of the building performance.

This report intends to serve as a basis for research in Adapt4EE, to be used by

partners as a reference state-of-the-art in the topics addressed by the project

and will provide the necessary knowledge for the definition of the Adapt4EE

architecture and to find the most suitable technologies that shall be integrated

into the Adapt4EE framework. In this context, this report also provides a set of

recommendations as guidelines for the developments that will take place during

the project lifetime, taking into account the analysis of the market trends related

to the areas of high interest for Adapt4EE.


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Table of Contents List of definitions & abbreviations............................................................ 4 Executive Summary.................................................................................. 7 List of figures ......................................................................................... 10 List of tables .......................................................................................... 13 1. Introduction.................................................................................. 14

1.1 Adapt4EE project concept ............................................................... 14 1.2 Scope of the Report........................................................................ 16 1.3 The Structure of the Deliverable....................................................... 17

2. State of the Art Analysis & Literature Review ............................... 21 2.1 Building Design and Modelling ......................................................... 21

2.1.1 BIM definition & origin ............................................................. 21 2.1.2 Overview of the BIM Paradigm.................................................. 23 2.1.3 Building Data Modelling Standards and Standardization ............... 26

2.2 Building Performance Simulation...................................................... 40 2.2.1 Definition and Importance of Simulation .................................... 40 2.2.2 Building Energy Performance Simulation Models ......................... 40 2.2.3 Occupancy Modelling and Simulation ......................................... 43

2.3 Multi-Agent based modelling and Simulation...................................... 51 2.3.1 An agent-based approach to modelling & simulation of building performance ....................................................................................... 51 2.3.2 Literature review on taxonomy of Agents ................................... 52 2.3.3 A literature review on Agent Platforms relevant for simulation and analysis in Adapt4EE project ................................................................. 54 2.3.4 Literature review on agent-based simulation tools and engines for the Building Energy Performance domain................................................ 60

2.4 Semantic-enabled technologies for building management.................... 65 2.4.1 Semantically enhanced building & device models ........................ 65 2.4.2 Ontology Management............................................................. 75 2.4.3 Services Management of Heterogeneous Devices ........................ 81 2.4.4 Relevant European Projects...................................................... 85

2.5 Enterprise Business Modelling.......................................................... 94 2.5.1 Enterprise Modelling Approaches............................................... 94 2.5.2 Enterprise Modelling Applications and Scenarios ....................... 102

2.6 Visual Analytics Technologies......................................................... 105 2.6.1 Introduction and terminology ................................................. 105 2.6.2 Definition of Visual Analytics .................................................. 106 2.6.3 Data visualization in the AEC industry ..................................... 108

3. Market Analysis........................................................................... 113 3.1 Introduction ................................................................................ 113 3.2 Building Information Modelling (BIM) and Building Performance Simulation (BPS) .................................................................................. 113

3.2.1 Major BIM products & tools .................................................... 113 3.2.2 Major BPS products & tools .................................................... 121 3.2.3 Market Penetration Data ........................................................ 160 3.2.4 Gap Analysis, Market Success Factors...................................... 164


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3.2.5 Conclusions, Future Market Needs, Trends and Opportunities ..... 167 3.3 Enterprise Business Modelling........................................................ 171

3.3.1 Major Products and Commercial Tools ..................................... 171 3.3.2 Future Market Needs, Trends and Opportunities........................ 172

4. Recommendations for tools and technologies to be integrated into Adapt4EE architecture.......................................................................... 176

4.1 Introduction ................................................................................ 176 4.2 Advancements in the field of middleware for accessing data from heterogeneous building sensors and embedded devices ............................ 176

4.2.1 Choice of Middleware............................................................. 176 4.2.2 Advancements in Middleware.................................................. 181 4.2.3 Enhancement in building & device models semantics ................. 183

4.3 Guidelines for Multi-agent based Modelling and Simulation of Energy Efficiency in Buildings ........................................................................... 186

4.3.1 Role and Objectives of agent-based modelling in Adapt4EE ........ 186 4.3.2 The added value of Agent-based building energy performance models and simulation ....................................................................... 187 4.3.3 Evaluation Results on existing Multi-Agent Based Modelling and Simulation Tools................................................................................ 191

4.4 Guidelines and Recommendations for Business Process Modelling (BPM) in Adapt4EE ............................................................................................ 192 4.5 Guidelines and Recommendations for Occupancy Modelling in Adapt4EE 194 4.6 Guidelines and Recommendations for Visual Analytics in Adapt4EE..... 194

5. Summary and Conclusions .......................................................... 196 References ........................................................................................... 198


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List of figures Figure 1: Adapt4EE Project Concept Overview .............................................. 15 Figure 2: BIM definitions in the literature ..................................................... 21 Figure 3: AEC industry activities influenced by BIM........................................ 23 Figure 4: Virtual Building Modelling as part of the BIM process [7] .................. 24 Figure 5: Data exchange with IFC between key stakeholders .......................... 29 Figure 6: Overview of the IFC layers and corresponding classes ...................... 31 Figure 7: Overview of the gbXML schema (version v5.0) ................................ 36 Figure 8: Models used by Zimmermann ([22]) for occupancy modelling ........... 45 Figure 9: Interface for user activity specification and occupancy scheduling used in UASEM approach ................................................................................... 46 Figure 10: Schematic model used by authors in [39] ..................................... 47 Figure 11: Agent type structures used by Zimmerman ([22]) to support building occupancy analysis and simulation .............................................................. 48 Figure 12: Floor plan of the University of Florida Building used by authors in [44] for the validation and verification of the energy prediction of the MuMo model. . 49 Figure 13: Generic Agent Overview ............................................................. 53 Figure 14: An ontology-based representation of the IFC classes ...................... 67 Figure 15: A conceptual ontology to outline predesign phases of a building using concepts of the AEC industry. ..................................................................... 67 Figure 16: Device ontology introduced by FIPA (Foundation for Intelligent Physical Agent) in 2001.......................................................................................... 70 Figure 17: Overview of the device consumption profile based on the ontology defined in Semantic Energy Information Publishing Framework by Bonino et al. 70 Figure 18: Ontology overview used in tbe DEHEMS European project, including conceptual alignment with SUMO upper level ontology schema. ...................... 71 Figure 19: Overview of ontology defined by W3C Semantic Sensor Network Incubator Group for modelling sensor devices, systems and processes. ........... 74 Figure 20: Overview of the ontology used in the Hydra project for the device ontology closely related to energy consumption information ........................... 75 Figure 21: Snapshot of Protege platform for creating ontologies...................... 80 Figure 22: Ontology management and visualisation using the TopBraid composer software................................................................................................... 81 Figure 23: Smart Items architecture of the unified middleware of accessing heterogeneous devices .............................................................................. 83 Figure 24: LinkSmart Middleware overview (HYDRA Project)........................... 86 Figure 25: SaveEnergy Project (CIP) architecture overview ............................ 90 Figure 26: The dimensions of the classification framework.............................. 95 Figure 27: Perspectives and aspects of the Zachman framework ([146]).......... 96 Figure 28: Perspectives and Aspects of the BPM Frameworks ARIS ([145])....... 96 Figure 29: Aspects and Perspectives of BPMS ............................................... 97 Figure 30: Models associated to perspectives and aspects .............................. 97 Figure 31: Sample Enterprise Model Components........................................ 102 Figure 32: Application Scenarios of a Collaborative Enterprise Modelling Environment........................................................................................... 104 Figure 33: Visual Analytics: The best of both Worlds (from [150])................. 105


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Figure 34: The Visual Analytics iterative process towards improving decision making of involved users.......................................................................... 106 Figure 35: Combination of colouring, texts and segmentation (peg glyphs) to visualize office occupancy in a 3D building model [174]. .............................. 109 Figure 36: Narrative of the method proposed by Akbas et al. ([45]) for temporal visualization of building performance highly related to its occupancy. ............ 110 Figure 37: Carpet-contour [177] plot illustrating mean value of HVAC electricity consumption against other information such as solar irradiance and dry-bulb temperature. The visualization technique has been proposed by authors in [177].............................................................................................................. 111 Figure 38: General data flow of Building Performance Simulation engines....... 122 Figure 39: Major categories for evaluating the capabilities of building energy simulation tools ...................................................................................... 123 Figure 40: The most important selection criteria for BPS tools by AEC community............................................................................................................. 124 Figure 41: DesignBuilder GUI – Recent files and templates for data input....... 127 Figure 42: DesignBuilder, overview of the interface tabs during model editing process. More information for the latest features and documentation can be found in official software website [138]............................................................... 128 Figure 43: BIM model visualization screen overview .................................... 129 Figure 44: Heating design tab and simulation results output through DesignBuilder interface ............................................................................ 130 Figure 45: Cooling design tab overview and simulation results in Design Builder regarding temperature, heat gains, humidity and ventilation. ....................... 131 Figure 46: Parametric simulation results within DesignBuilder, an overview for variations in the different design variables.................................................. 132 Figure 47: Computational fluid dynamics within DesignBuilder. ..................... 132 Figure 48: Daylighting simulation results – an overview from DesingBuilder tool............................................................................................................. 133 Figure 49: ECOTECT software overview – workspace, tabs and menu ............ 136 Figure 50: OpenStudio plugin running on the Google Sketchup Pro software... 140 Figure 51: Snapshot of the simulation results through the Results Viewer tool of OpenStudio toolset (Source: NREL Open Studio documentation). .................. 141 Figure 52: Bentley AECOSim Energy Simulator functionalities overview ......... 143 Figure 53: Lider’s software user interface................................................... 146 Figure 54: Lider project description form.................................................... 147 Figure 55: Lider’s verification of the design requirements in terms of energy efficiency ............................................................................................... 148 Figure 56: CALENER user interface for energy efficiency analysis .................. 150 Figure 57: CALENER system view.............................................................. 150 Figure 58: CALENER BER estimation and building assessment....................... 151 Figure 59: CALENER visualization for the summary of the energy consumption and demand of the input building model. ................................................... 151 Figure 60: CALENER export of energy analysis in pdf format......................... 152 Figure 61: CERMA software snapshot (first screen)...................................... 154 Figure 62: CERMA city / environmental screen parameters for energy efficiency analysis ................................................................................................. 154 Figure 63: CERMA software global information screen.................................. 155 Figure 64: CERMA visualization of the BER analyis for the given building model............................................................................................................. 156


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Figure 65: CERMA detailed demand of the different services......................... 156 Figure 66: Snapshot of the Energy Building workspace provided by CivilTech [143]..................................................................................................... 157 Figure 67: Overview of the 3D model perspective of the Energy Building software (Source: Civiltech Energy Building official manual [143]) ............................. 158 Figure 68: Energy performance analysis within “Energy Building” tool for internal cooling and heat loads for the input BIM model (Source: CivilTech official manual [143]) ................................................................................................... 159 Figure 69: BPS tools developed between 1997- 2010................................... 163 Figure 70: Ranking the Importance of Features of BPS Tools ........................ 164 Figure 71: Major types of modelling tools, based on (BPTrends, 2007)........... 172 Figure 72: Conceptual architecture of the Adapt4EE device cloud .................. 182


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List of tables Table 1: List of most common exchanged formats in AEC tools provided by technology vendors ................................................................................... 27 Table 2: Brief overview of the IFC model coverage, advantages and limitations. 33 Table 3: Agent-based modelling and execution platforms overview.................. 56 Table 4: Agent-based platforms on social interaction & simulation overview. .... 58 Table 5: Past and Ongoing European Projects using LinkSmart Middleware in various application domains........................................................................ 87 Table 6: Overview of the modelling approaches and standards in Enterprise Modelling ................................................................................................. 99 Table 7: List of major BIM tools used in the market during the whole life cycle of a building (from early design to the operational phases) .............................. 113 Table 8: Business Process Modelling Tools and Capabilities........................... 174 Table 9: Summary of middleware approaches based on several criteria and factors affecting overall middleware usage in diverse application domains including energy efficiency in buildings ...................................................... 176 Table 10: Sensor classification along three dimensions ................................ 180


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1. Introduction The purpose of this deliverable is to gain a detailed insight of the state-of-the-art

technologies relevant to the topics and domains addressed by Adapt4EE. The

ultimate goal is to provide a thorough literature review on critical domains such

as building performance simulation tools, ontology management and semantically

enriched building information models as well in occupancy & business modelling

and simulation in order to be able to decide on which technologies the Adapt4EE

framework can build upon and how these tools must be improved for Adapt4EE to

fulfil its main objectives. Next paragraphs of this Chapter introduce the reader to

the overall concept of Adapt4EE, whereas in the following sections the main scope

of the report is provided along with its structure.

1.1 Adapt4EE project concept Energy Efficiency is considered to be a key component of the European energy

policy underlying the fundamental objectives of the European Union’s (EU) 2020

strategy. Recent and past surveys indicated that buildings are a major constituent

of the urban ecosystem accounting for almost 40% of the overall energy demand

in Europe [1]-[2]. Thus, construction products (and especially those of

commercial use) constitute energy intensive systems through their whole life

cycle, comprising energy demanding assets and facility operations but, most

importantly, occupants that are the driving operational force, performing

everyday business processes and directly affecting overall business performance

as well as overall energy consumption.

Extensive industrial practice throughout the years and respective market surveys

demonstrated that most crucial decisions concerning construction products

happen in the early phases of the design process. Specifically, the findings of

recent research studies indicated that appropriate design improvements, tailored

with the support of building performance simulation software, could reduce

energy use in both existing and in new building envelopes [3]-[4]. In this

context, early design products comprise features that determine to a large extend

energy performance and thus can provide critical evidence to simulation and

analysis tools for thorough evaluation of design alternatives. To cope with this,

modelling and simulating the energy efficiency of buildings and various facilities

semantics has now been established as an integral part of the design process and

many simulation tools are commercially available as a common practice by

designers & engineers.

Adapt4EE aims to address several shortcomings of existing and rather complex

building tools such as the lack of a holistic and systems-based view of buildings,


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the efficient separation of algorithms and simulation that will stimulate key

players (D&Es) to easily assess the energy use on specific attributed domains.

The main purpose of the project is to develop a building simulation framework

focusing on the early design phases of a construction product, which will be able

to provide the key stakeholders with the necessary simulation results that fully

take into account both i) the descriptive data of a building (material, components,

equipments, space layout, etc) and ii) the information related to the dynamic

behaviour of the building due to its occupancy fully taking into account the

organization that is going to be “housed” within the building. The overall concept

of the Adapt4EE is illustrated in Figure 1.

Figure 1: Adapt4EE Project Concept Overview

As seen, Adapt4EE aims to deliver and validate a holistic building simulation

framework that takes into account the fusion of two different but complementary

worlds: i) the Building Information Modelling (BIM) and ii) the Business Process

Modelling (BPM), having as main catalyst the human factor (presence and

movement). The incorporation of information about the dynamic behaviour of a

building (e.g. organization that will be “housed” in the building) at the early

design stages of a building will further improve the ability of designers, engineers

and respective stakeholders (business modellers and/or building owners and

tenants) to analyze the energy performance of the construction products as well

as to allow them for optimisation of its energy consumption based on multi-

dimensions / multi-criteria constraints. Moreover, design decisions on energy

performance optimization should be based on sound and realistic estimations of

the actual future energy consumption of constructions during operation, taking

also into account potential consequences on business operations affected by early

design decisions and vice versa. To cope with this, Adapt4EE aims at further


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augmenting the contemporary architectural envelope by incorporating business

and occupancy related information into the building under design that will

eventually provide the necessary shareable knowledge for effectively analyzing

the energy consumption of enterprise buildings as well as for further reconciling

the differences between the energy performance of “real” and “simulated”

construction products.

1.2 Scope of the Report This document reports the findings of the literature review and market survey

carried out in Adapt4EE on all critical aspects and topics addressed by the project.

It is based on partners’ knowledge, literature survey, product research and

cooperation among the consortium on diverse but complementary to the project

tools and technologies that will be utilized in order to deliver and validate the

Adapt4EE framework. The deliverable can be considered as an extensive and

thorough analysis of the current state-of-the-art in the topics addressed by the

project.

The document focuses mainly on existing tools, technologies and frameworks

used to assess the performance of a building (i.e. energy usage, space utilization

due to its occupancy, etc) at early stages of the design and prior its realization,

whereas specific sections of the report provide a literature review on technologies

used to simulate the building performance taking into account user behaviour

(occupancy models), enterprise modelling (BPM) as well as semantic-enabled

technologies used for building management and analysis. The ultimate goal of the

detailed analysis of existing tools and technologies in these fields will establish a

groundwork for research and development that will be conducted in Adapt4EE.

Thus, this report can be used by partners as a reference for state-of-the-art

technologies in their respective fields of expertise and will help to design and

deliver the most appropriate solutions for the Adapt4EE system.

Another important objective of this report is to provide a basis for the market

research to be carried out within the project lifetime towards identifying market

needs, future trends and the added-value of the technologies that will be

designed, delivered and validated within the project. In this respect, the

deliverable, as part of the WP1 “Adapt4EE Definition”, provides a list of guidelines

that should be taken into account during the final definition of the Adapt4EE

architecture, towards providing a usable, extendable, interoperable and shareable

building simulation framework capable to fulfil the needs of its end-users, as they

were defined and reported in the corresponding Adapt4EE deliverable D1.1 “User

and Business Requirements” [122].


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Next section gives a deep insight for the overall layout and the structure of the document.

1.3 The Structure of the Deliverable The structure of the deliverable is as follows:

Chapter 1: Introduction

Chapter 2: State-of-the-art analysis & literature review

The literature review starts with a global overview of the Building

Information Modelling (definition, paradigm, etc.). Section 2.1

“Building Design and Modelling” outlines the dominant building

modelling standards used in the Architect, Engineering &

Construction (AEC) industry and describes in detail the advantages

and disadvantages of each standard.

Further in this Chapter, Section 2.2.2 ”Building Energy

Performance Simulation Models” briefly reports existing energy

performance simulation models used by Building Performance

Simulation (BPS) tools, whereas Section 2.2.3 “Occupancy

Modelling and Simulation” reports a thoroughly literature review

on algorithms used for occupancy modelling and simulation. This

section provides also a brief overview (i.e. taxonomy) of the

various approaches used to model the building occupancy and

discusses future challenges on enriching occupancy models with

additional metadata (e.g. critical business processes that will be

“housed” in the building under design) towards improving the

prediction on energy analysis of a building at the early stages of

the design process and minimizing the differences between the

“simulated” and the “real” energy consumption.

Moreover, an extensive survey is presented in Section 2.3 “Multi-

Agent based modelling and Simulation” for agent-based

technologies that can be used in Adapt4EE as part of the

simulation framework (e.g. agents for the physical, the human,

the enterprise and the general surrounding environment in order

to enable the energy analysis of a building using agent-based

approaches). Existing platforms suitable for the Adapt4EE

framework are analyzed and a discussion is made for the

advantages and drawbacks of each platform. In this section, a list

of criteria and characteristics that should be available by the agent

platforms are outlined towards the selection of the most suitable


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solution (-s) for the Adapt4EE system.

Next Section 2.4, entitled "Semantic-enabled technologies for

building management” describes in detail semantically enhanced

technologies employed in the definition of building models with

ontologies taking into account the heterogeneous devices that may

coincide in an enterprise building during its operation phase. The

main purpose of this Section is to outline SoA technologies that are

highly related to the Adapt4EE middleware, which will be utilized

to deliver a semantically-enriched building measurement

framework capable to analyze energy consumption in real-life

scenarios (Adapt4EE pilots) and will provide the necessary

mechanisms for the effective calibration of the respective

simulation models.

Moreover, Enterprise Business Modelling tools and technologies

that could be employed in the definition of corresponding

Adapt4EE enterprise models are investigated in Section 2.5. This

section analyzes current approaches used to model the activities

and processes encountered in an enterprise including the

resources and equipment related to the operations performed in its

spaces. Based upon the modelling approaches and frameworks

found in the literature, an extensive evaluation is given for the

most important technologies according to several dimensions

(perspective, language, formalization, etc.). Finally, application

and scenarios of use of the aforementioned frameworks are

outlined, whereas the enterprise data models views that should be

taken into account within Adapt4EE in respect to modelling the

human presence and movement (occupancy) are outlined in

Chapter 4.

One of the key innovations of Adapt4EE framework will be to

provide to its end-users enriched visualization methods towards

analyzing several diverse but complementary parameters that may

affect the building performance. The incorporation of visual

analytic technologies in building performance simulation is an

active research area, where several existing techniques for

analyzing complex information could establish reference

groundwork for the visualization techniques that will be developed

in Adapt4EE. A detailed review on existing SoA for visual analytic


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technologies is there provided in the last part of Chapter 2 (section

2.6).

Chapter 3: Market Analysis

This chapter initially describes the findings of recent surveys on

major BIM & BPS tools. The list of the available tools is

accompanied with a brief report on the available features,

including analysis for their usability, interoperability and

friendliness in respect to its end-users. As Adapt4EE focuses on

tools and technologies used at the early stages of design of a

building, only major tools that can be used by architects and

engineers (A&Es) are reported. A number of selection criteria used

by A&Es towards the classification of the tools used in their daily

work are outlined in this section.

Except the analysis of existing market tools, this section provides a

general overview for the BIM & BPS market and described in detail

the most important findings resulted from the market survey.

Current shortcomings as well as future needs in relation to topics

that will be addressed by Adapt4EE are outlined in this Chapter,

whereas a discussion is made for the major opportunities that will

arise by the deployment of enhanced BPS tools in the market that

provide holistic approaches for the assessment and evaluation of

the energy performance of construction products.

In addition to the market survey for BIM & BPS tools, a separate

section (3.2) is devoted to the existing tools used for enterprise

modelling and simulation. The major categories of BPM tools are

outlined in this part of the deliverable, targeting mainly on

capabilities that are relevant to the Adapt4EE scope. This section

concludes on future trends in Business Process Modelling tools,

including ideas for the extension of building information data with

enterprise data that are necessary to increase the collaboration

among key stakeholders during the design phases of a

construction product in all aspects including but not limited to the

energy analysis, validation against the enterprise performance

taking into account the human activities through the building

spaces under design.

Chapter 4: Recommendations for tools and technologies to be


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integrated into Adapt4EE architecture

Based on the literature review and the market analysis, this

Chapter provides a set of recommendations for the various tools

that will compose the Adapt4EE integrated framework. For each

technology, methods and tools presented in previous chapters, a

set of guidelines are provided along with advancements to be

performed in various topics such as the Adapt4EE middleware, the

occupancy and business modelling framework including agent-

based approaches and visual analytics.

Chapter 5: Summary and Conclusions

This chapter summarizes on the content described in this report

and concludes on the challenges for Adapt4EE in the light of the

state-of-the-art and the market findings.


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2. State of the Art Analysis & Literature Review 2.1 Building Design and Modelling

2.1.1 BIM definition & origin BIM definition

BIM is the process of generating and managing building data during its cycle of

life using dynamic building modelling software in three dimensions and in real

time, to reduce wasted time and resources in the design and construction. This

process produces the building information modelling (BIM), which encompasses

building geometry, spatial relationships, geographic information, quantities and

properties of building components.

BIM can be used to illustrate the entire process of building, maintenance and

even demolition. Quantities and shared properties of materials can be extracted

easily. In addition, workplaces, details of components and construction activity

sequences can be isolated and defined. Thus, BIM as a concept is not a type of a

specific software but a process used to generate, manage and share building

related information in the AEC industry.

Moreover, BIM is an integrated process that is currently used to expedite the

exchange of design and construction information to the key stakeholders

(building designers, architects, tenants, owners, etc). It can be considered as a

“medium” which contains consistent, reliable and sufficient data to support any

activity along the whole lifecycle of a construction product (from the early design

stages to the operational phases of a building). An overview for the context of

use of the BIM acronym is illustrated in Figure 2.

Figure 2: BIM definitions in the literature


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As seen, there are two abbreviations used for BIM, one for denoting the actual

building data (i.e. doors, walls, spaces, zones with associated relationships,

attributes and properties) in terms of digital representation (BIM as Noun) and

the other related to the use of BIM as a verb, denoting any activity performed

among key stakeholders for the creation, management, derivation and sharing of

information related to a construction product. Depending on the level of

development of a building, different key stakeholders may collaborate to create

and maintain the BIM information in order to ensure the quality and efficiency of

the BIM information related to the whole building envelope.

BIM Origin

There are two views on the origin of this concept: it is said that Autodesk was the

first to use the term to refer to BIM 3D object-oriented design, while others

postulate that it was Professor Charles M. Eastman, Georgia Technical Institute of

Technology, the first to spread the concept of BIM in the early seventies in

several books and technical articles.

However, there seems to be general consensus that Jerry Laiserin was the person

who popularized it as a common term for the digital representation of

construction processes, with the aim of exchanging and interoperability of

information in digital format.

Different technology suppliers such as Sigma Design, Autodesk, AceCad StruCad

Software, Bentley Systems, Graphisoft, Tekla, Nemetschek, and CADDetails,

among others, offer this capability.

Moreover, the concept of BIM in the area of architecture and construction has

several options for platforms and software for implementation. The BIM use

started with the design of complex building such as hospitals, big malls and

enterprise offices.

Real usage of BIM (Current Practises)

The advantages of the use of BIM are the unitary and overall concept of the

building project. A high-level overview illustrating the phases in which BIM tools

and technologies are used as of today is illustrated in Figure 3. In general the

three most important phases are considered, namely “Planning & Design”,

“Construction”, and “Commissioning & Operation”, whereas the demolition phase

can be considered as the last stage of the operational lifecycle of a building. It

should be noticed that usually the BIM process doesn’t follow the whole life of the

building, but reaches until the building is delivered to the property

owner/operator.


April 2012 23 CERTH

Figure 3: AEC industry activities influenced by BIM

This can be attributed due to the fact that BIM process as of today is complicated

as it involves from one hand the union of the work of architects, engineers,

property owners in a collaborative manner and on the other hand in certain

stages of the design can be uncomfortable due to the lack of the necessary tools

for seamless cooperation among key stakeholders.

Searching in other areas of knowledge such as naval engineering and

management of people movements in commercial spaces or event organization

could be very useful for using BIM-based representation. It is important to take

into account the simultaneity and collaboration among project teams based on

the so called “Virtual Building Modelling” process [7], which is actually an

instantiation of the BIM models during the whole lifecycle of a construction

product.

Next sections present in detail the BIM paradigm in the AEC industry, outline

existing standards used for data share and knowledge via BIM tools Along with a

comparison among the most dominant technologies used today in the AEC

industry.

2.1.2 Overview of the BIM Paradigm

The future of the AEC industry will be considerably influenced by the use of

technology. The emergence of Building Information Modelling (BIM) is

expected to drive the construction industry towards a multidimensional and multi-

view “Model Based” process and gradually move the industry away from a simple

2D or 3D Based process. In the BIM paradigm buildings and surrounding areas,

despite their complexity, will be built virtually before they get actually

constructed in the field.

The BIM paradigm establishes a streamlined collaborative design process,

providing simultaneous and “personalized” access to all stakeholders involved.

Different user needs (clients, architects, engineers, constructors etc) are

efficiently addressed at different phases of the design process following an object


April 2012 24 CERTH

oriented and data-rich parametric view of the construction project supporting

collaboration and decision making on all aspects of the building life-cycle.

Figure 4: Virtual Building Modelling as part of the BIM process [7]

As presented in Figure 4, BIM-based project progression process is performed

through Virtual Building Modelling (VBM) [7], which represents both i) discipline

models used by key stakeholders in different stages of a construction product life-

cycle and ii) the integrated model used by AEC industry users for collaboration

such as the exchange of information and clash detection during BIM models

trading (e.g. architectural views with more detailed views including physical and

functional characteristics of a building’s facility). Thus, through VBM, BIM

establishes a collaborative framework for all members of the design and

construction teams and later on for owners/operators and for the whole life cycle

of a facility. The collaboration framework facilitates the streamlined and seamless

management of a single interoperable information base incorporating rich object

models carrying physical, design and performance attributes.

BIM offers several obvious benefits to the AEC industry and the design and

construction process, e.g.:

• The ability to identify design errors at the early design phase, where

extensive performance simulations are available (depending on the level of

development) and changes on the construction product can be performed

without significant costs.

• The ability to analyse and visualize building information in more than three

dimensions (e.g. 4D for time scheduling, 5D for component pricing and

budgeting reports, etc.). In this context, BIM tools contain tools for

simulation capabilities and engines capable to “simulate” simple or

complicated processes and provide data-rich views according specific

needs of the end-users.

• The ability to identify collisions and to perform clash tests when migrating

(trading) various discipline models and views of a BIM-based

representation.


April 2012 25 CERTH

• The ability for non-technical people (e.g. clients) to visualize the end

product and the support to provide a single database of information that

fully addresses the needs of all parties.

• To reduce actual errors and corrections during field construction thus

reducing overall costs and building management. Thus, BIM as a concept

and a process together can save cost and time at every phase of design

and construction of a building.

• It provides higher reliability of expected field conditions, allowing for

opportunity to do more prefabrication of materials offsite, which is usually

a higher quality at a lower cost.

• The ability for key stakeholders to perform simple or complicated “what if”

evaluation scenarios, such as looking at various sequencing options, site

logistics, space layout alternatives, equipment costs, etc.

• The ability to estimate more realistically and more accurately the

operational aspects of the building design, thus reducing possible warranty

costs

As BIM is continuously growing, interoperability, as illustrated in the integrated

model in Figure 4, proves to be a critical success and commercial viability factor.

It is expected that BIM adoption in AEC industry will foster and enhance

collaboration between design teams with various disciplines (architects,

engineers, planners, etc), as well as with the teams closely working within the

construction industry (building tenants, owners, facility managers, etc).

As of today, BIM technology has proven to enhance collaboration between design

disciplines and between designers and constructors. It also allows non-experts

involved in the process (clients, lenders, owners, etc) to actively and effectively

participate in the design process by providing their valuable feedback in critical

stages of the lifecycle of a building.

As the use of BIM continuously accelerates within the design and construction

industry, it is envisaged that will lead to a revolution in project delivery towards

providing a fully collaborative and streamlined project work, from the project’s

inception, to developing the facility design and finally delivering a fully

operational construction. Collaboration will ensure efficiency and speed of

delivery, effectiveness and reduced project costs without sacrificing quality or

consistency, while exchange of information in an interoperable manner (e.g. in

terms of underlying data models and information exchange standards to the

necessary information management tools), is expected to be in the core of

current and future work. The following paragraphs provide a bried literature


April 2012 26 CERTH

review on the exchange formats used in the AEC industry to support the share of

information among involved parties, whilst focus is given on the latest

developments regarding interoperability, where the dominant standards utilized

by major BPS tools are outlined together with their strengths and disadvantages.

Summarizing, BIM models have been widely used in AEC industry and several

additional dimensions related to the original 2D and 3D drawing models have

been delivered by key software vendors and corresponding standardization

bodies/institutions towards enriching the information contained in the

construction products and allowing for better prediction of their performance in

several diverse but complementary domains in the AEC industry. Next paragraphs

provide a historical overview on the data formats used in the AEC domain and

describe in detail the most dominant standards used recently in the market

towards addressing the share of knowledge and information among key

stakeholders.

2.1.3 Building Data Modelling Standards and Standardization Standards have played and will continue to play important roles in AEC business

practice. Standards in the AEC industry may refer but not limited to material

performance standards, graphic standards, standards for defining products,

drawing set standards, classification standards, layering standards, etc. Some

standards have been created in order to help people and project teams to

understand each other. Since building information model standards are digital,

the development of such standards also are digital.

As of today, Software Engineering and Information technology providers from one

hand have implemented a groundwork technological framework for

interoperability, by providing the languages that support exchange protocols

among tools and framework (EXPRESS, BPMN, IFC, XML-based and many others).

On the other hand, architects, engineers, contractors and fabricators, are the

knowledge experts that know what the information content of an exchange

should be. From the AEC industry perspective, key organizations and market

leaders in building design tools could have the economic clout or knowledge to

define effective interoperability for their products, but given the evolution of the

existing tools this will be unfeasible for the whole industry. Thus, exchange

standards defined by key stakeholders in the AEC industry such as building

software vendors owners seem an imperative.

It is evident that multiple applications with overlapping data requirements should

be able to support various tasks of design and construction but also to address

the active collaboration of key stakeholders. Herein, the term “Interoperability”


April 2012 27 CERTH

refers to the ability of exchanging data between applications in order to fully

support their workflow processes, which further facilitates their automation (in

terms of both input and output). Recent surveys on interoperability in the AEC

industry indicated that data share has traditionally relied on file-based exchange

formats limited to geometry, such as DXF (Drawing eXchange Format) and IGES

(Initial Graphic Exchange Specification). Direct links based on the Application

Programming Interfaces (APIs) were the oldest and still confident way to

interoperability. Starting in the late 1980s, data models were developed to

support product and object model exchanges within different industries, led by

the ISOSTEP international standards effort. In this context, the building-related

information models were utilized to distinguish both i) the schema used to

organize the data and ii) the schema language to carry the data. Data parsers

were always available and recently several APIs have been proposed for the

translation from one schema language to another, for example from Industry

Foundation Classes (IFC) to XML and vice-versa.

Recent McGraw-Hill surveys on BIM have identified “interoperability” as the

largest issue for advanced BIM users (McGraw-Hill 2009 [5]). A summary of the

most common exchange type of formats in the AEC industry used in the past

years is listed in Table 1.

Table 1: List of most common exchanged formats in AEC tools provided by technology

vendors

Name / Format Type Description

Image Formats

JPG, TIF, BPM, PNG, RAW,

etc.

Raster formats vary in terms of compactness, number

of possible colours per pixel, transparency,

compression with or without data loss.

2D Vector Formats

DXF, AI, EMF, WMF, DWG,

SWF, PDF, etc.

Vector formats vary regarding their complexity and

may include line formatting, colour, layering and types

of curves supported. Several 2D exchange formats are

file-based (proprietary or based on standards) and

others use XML.

3D Surface and Shape

Formats

3DS, WRL, PTS, DWF,

SWG, OBJ, etc.

3D surface and shape formats vary according to the

types of surfaces and edges represented, whether they

represent surfaces and/or solids, material properties of

the shape (colour image bitmap, texture map, etc), or

viewpoint information. Existing data information files

may be using ASCII, binary or both encodings.

Moreover, several formats may also contain lighting,

camera, and other viewing controls, whereas several


April 2012 28 CERTH

file formats are based on XML (e.g. wrl).

3D Object Exchange

IFC, EXP, etc.

AecXML, Obix, bcXML,

gbXML

Product data model formats represent geometry

according to the 2D or 3D types represented. These

data exchange formats may also carry object type data

and relevant attributes and properties, whereas

relations among objects can be supported. They are

the richest in information content and are currently

used by major building performance tools and related-

products.

Several XML schemas developed for the exchange of

building data (from the home automation to the

enterprise building design and automation). As of

today, these formats vary according to the information

exchanged, the workflows supported and the context of

use (e.g. energy efficiency evaluation, cost evaluation,

etc).

As seen on Table 1, as of today several exchange formats have been used in the

AEC industry for data sharing and collaboration among key stakeholders. These

include 2D raster image formats for pixel-based images, 2D vector formats for

line drawings, 3D surface and solid shape formats for 3D forms. Three-

dimensional object-based formats are especially important for BIM uses and have

been used widely by the AEC end-users for different purposes and applications

(early design performance analysis, building monitoring and site management,

home automation, etc.). It should be noticed that while 3D geometry of

assemblies is complex, the additions of properties, object types, and relations has

led to a large increase in the types of information represented and available for

analysis and simulation by the building-related tools and technologies.

Several companies like Graphisoft, Autodesk and Bentley have developed

enriched software packages, tailored to the needs of their end-users and focusing

on several functionalities provided by the BIM related tasks. However, till today,

architects, engineers and other key stakeholders face several shortcomings of

their preferred tools when diverse but complementary BIM related tasks can not

be performed in a unified manner (e.g. to perform space utilization simulation in

a building under design and in parallel to analyze in detail the energy


April 2012 29 CERTH

performance of the construction product due to various factors (e.g. thermal

loads, lighting, HVAC systems, occupancy schedules, etc). Key stakeholders in

the AEC industry have been enganged in the last years with a great amount of

best practices using BIM-related products and software. However, several efforts

have to be made by the industry and the software vendors towards fulfilling the

requirements for the generation of a fully integrated BIM model. In this direction,

the evolution of existing standards in the AEC domain can further facilitate the

process of making BIM to become the core integrated design process that it was

originally designed for.

Focusing on the early design stages of a construction products, two dominant

standards used in the AEC industry are outlined in the next sections, namely IFC

and the Green Building XML (gbXML).

2.1.3.1 Industry Foundation Classes (IFC)

History and General Information

The Industry Foundation Class (IFC) is an industry-developed product data model

that can be used to support the whole lifecycle of the construction products (i.e.

from early design phases to the commissioning and operation stages of a

building).

Figure 5: Data exchange with IFC between key stakeholders

It is currently supported by buildingSMART [8], whereas the schema was

designed and implemented in order to define an extensible set of consistent data

representations of building information that can be used for exchange of the

necessary information among the AEC software applications. It relies on the ISO-


April 2012 30 CERTH

STEP EXPRESS language and concepts for its definition, with a few minor

restrictions on the EXPRESS language. Moreover, the IFC entities and their

definitions are based on the International Framework for Dictionaries (IFD) work

done by buildingSMART International. IFD has been defined in order to establish

standards for terminology libraries or ontologies and thus supports unambiguous

computer interpretation of elements in the models in context of external

professional resources. The ontologies are also meant to include support for

language independence.

IFC was designed as an extensible “framework model”, which initially registered

by ISO (International Organization for Standardization) as Publicly Available

Specification (ISO/PAS 16739), prior to its development as a full International

Standard. The current stable release is IFC2x3 version 3 [9], whereas recently

the IFC4 version 4 is available to the wider public as a release candidate. As of

today, IFC is currently used by a large number of building-related software

vendors including major architectural design suites. Its developers intended it to

provide a broad, general definitions of objects and data from which more detailed

and task-specific models could be defined, supporting particular exchanges

among key stakeholders could be defined. The object-based approach addresses

the problem of unambiguous encapsulating the complex building information data

that can be shared by several professionals in the AEC domain, as illustrated in

Figure 5. Concluding, IFC standard and underlying models have been designed to

address all building information, over the whole building lifecycle, from feasibility

and planning through design analysis and simulation (including occupancy

analysis and simulation), to construction and building operation management in

commissioning phases of its lifecycle [10].

IFC Core Schema & Definitions

In general, IFC data models refer mainly to a library of objects and properties

definitions that can be used to represent a building project and support use of

that building information for particular purposes. IFC can be used to provide

several views of a single IFC project such as (i) the architectural view, (ii) the

mechanical system view, and (iii) the structural view. The actual view used

depends on the actual context of use and the level of development of the building

under design.

An overview of the system architecture perspective is illustrated in Figure 6. As

seen, each resource and core sub-schema has a definition of relative entities that

consequently define data models for the building domain, as they are specified at

the Interoperability & Domain layers.


April 2012 31 CERTH

The core data schemas establish the most general layer within the IFC schema

architecture. Entities defined in this layer can be referenced and specialized by all

entities in the shared element layer and the domain specific layer.

The resources layer (from lowest to highest) defines the necessary reusable

constructs in terms of geometry, topology, materials, equipment, actors and their

roles, presentations and several other properties. These definitions are based on

EXPRESS definitions and are abstract enough for all kind of construction products

and consistent with the ISO-STEP library resources.

The core layer contains entities that address non-industry and industry-specific

concepts and can be used to define other entities in the higher layers of the

architecture. As an example, the Kernel schema defines core elements and

objects such as actors, group, product and relationship, whereas the product

extension schema defines specific building components such as zones, spaces,

building elements, etc. Other extensions schemas contain concepts related to

process and control related entities such as task, procedure, schedules, and so

on.

Figure 6: Overview of the IFC layers and corresponding classes


April 2012 32 CERTH

The entities that rely on the resource layer (base entities) are constituted

together to form commonly used objects in AEC domain, namely Shared Objects,

that reside in the Interoperability layer. These objects due to the object-

oriented nature of the IFC can be elaborated (extended, sub-classed, etc) to cope

with all building elements such as floors, structural elements, process elements,

walls and other features. In this layer for instance reside the Shared Building

Facilities elements, in which concepts for asset, occupant and furniture type are

defined. In this layer the most common building entities would be defined.

Finally, on the top level of the IFC hierarchy, the domain-specific extensions

reside, which address various entities that co-exist in a building under design

such as architectural, electrical, mechanical and other building extensions for

facility management.

Currently the domain’s layer includes the following aspects of the AEC:

� AR—Architecture

� BS—Building Services

� CM—Construction

o CM1—Procurement Logistics

o CM2—Temporary Construction

� CS—Codes and Standards

� ES—Cost Estimating

� PM—Project Management

� FM—Facility Management

� SI—Simulation

� ST—Structural Engineering

� XM—Cross Domain IFC Coverage & Limitations

IFC data format is composed of a set of complex data models and entities that

can address every “business transaction” encountered within the building domain.

To cope with the necessity that some of the tasks or processes encountered

within the AEC domain only need subsets of the available data, buildingSMART

created “exchange requirements” to support the share and exchange of the

necessary subsets of the IFC models. The most commonly implemented view of

the IFC model is the one termed “Coordination View” and entails the exchange

requirements for “architecture”, “building services” and “structural” models.

Additional views include the IFC “Structural Analysis View”, the “Basic Facility

Management Hand-Over View”, etc. It is evident that as IFC is able to represent a

wide range of building design, engineering, and production information, the range

of possible information to be exchanged in the AEC industry is huge. As of today,


April 2012 33 CERTH

the IFC coverage increases with every release and progressively addresses

limitations, in response to key stakeholders and developer needs.

Next Table provides a brief overview for the advantages and limitations of the IFC

model, whereas a detailed description on the IFC coverage can be found in [7]. It

should be noticed that all application-specific objects, when “converted” to an IFC

model, are consisted of the respective object type and associated 2D/3D

geometry, relations, and properties.

Table 2: Brief overview of the IFC model coverage, advantages and limitations.

Geometry IFC geometry is an adapted version of ISO 10303 part 42 (STEP). It

has a full range of geometry classes including extrusions, solids

defined by a closed connected set of volume enclosing faces such as

B-Reps and Constructive Solid Geometry (CSG), as well as shapes

defined by a tree of shapes and Union intersection operations

(Feature Addition and Subtraction and/or Constructive Solid

Geometry). It can combine geometry/topology into representation

maps and parts of shapes may be distinguished as shape features,

thus it can individually reference different aspects of shape within a

representation. These cover almost all construction needs and most

design needs.

Discussion on Geometry Coverage in IFC

The IFC geometry was defined and implemented in a way that could

support the exchange of simple parametric models between

systems, such as wall systems and other extruded shapes. Focusing

on a simple geometry model definition, not all needed information

in the AEC domain can be exchanged (e.g. rules, constraints, etc),

requiring additional efforts to exchange editable parametric models.

Few commercial tools have made use of the parametric capabilities,

and in the future these features should further explored by AEC

software vendors and providers.

Relations Relations are typed and link one object with another. IFC relations

represent a rich set of relations among objects and special attention

has been paid by several BIM design tools to incorporate them

during the translation from the internal schemas to the IFC format.

Their actual use in BIM design is still open and several

enhancements may be needed to future releases of IFC format.

Properties IFC places emphasis on property sets, namely P-sets. These are


April 2012 34 CERTH

composed of a rich set of attributes that are combined to define

material, contextual properties such weather data and in general

can be used for a particular type of performance. P-sets are defined

for most of the building elements such as roof, window, wall, etc.

Moreover, several attributes are associated with different material

behaviours, such as for thermal material, mechanical properties,

fuels, and others.

The current version of IFC lacks some properties such as tolerance

properties or attributes to explicit represent uncertainty, which

could be useful by the building performance simulation tools and

engines. These missing properties can be included by manual

editing and without following any specification and could lead to

interoperability issues. Similar functional limitations apply to

mechanical systems. It is expected that future releases of IFC will

cope with such issues in order to enable key stakeholders to use

missing properties in an efficient and effective way.

Metadata IFC format includes metadata in order to address the use of

information over time. Thus, the metadata information in IFC allow

for information ownership, tracking of changes, controls and

approvals. It allows also defining several constraints and objectives

for analyzing intents. However, their applicability by commercial

tools in AEC domain is not yet fully explored.

Regarding the architectural level of detail, IFC has well-developed

entities and elements defined. The representation in full details for

fabrication and manufacturing is not fully covered; however the

basic entities (resources layer) can be extended in order to provide

more detailed IFC product schemas, or can be expressed via

standalone schemas (i.e. CIMsteel Integration Standard - CIS/2).

The metadata information can be used in order to describe the

information represented in different but complementary design

applications (e.g. space planning and performance analysis tool for

building owners with energy performance analysis for architects and

engineers). The need for an OpenBIM implementation may lead to

further elaborate the schema for metadata information (e.g. to

reside in a repository for seamless access by key stakeholders) and

enhancements must follow up the needs of end-users. Thus, if

extensions are needed to deal with the limitations noted, these can


April 2012 35 CERTH

be added through a regularly scheduled extension process fully

addressing the needs of key stakeholders.

Summarizing, IFC has been designed and developed in a way to respond to the

different needs of designers, contractors, building product suppliers, fabricators,

tenants and building owners. To cope with different user needs and requirements

the IFC data format is both rich and redundant. Rich, as it contains the

information needed by respective stakeholders to analyze the building envelope

(interior, exterior) in all stages of its lifecycle. Redundant, as most of the

information contain several alternative entities and elements (e.g. geometry has

many types of properties and relations) that can be used for particular exchanges

or integration procedures. Several model views are missing, in which subsets of

the IFC schema may be exported to support respective stakeholders (e.g.

architect’s structural export for space layout and energy performance analysis).

Such exchanges should be carefully specified and should be supported in future

releases of the IFC model towards providing concrete and useful views of the IFC

schemas. Near future will indicate whether IFC-format will “survive”, or the direct

proprietary API-based integration between software vendors will be the future in

interoperability among AEC industry. This is also intensified by the fact that

recently buildingSMART® International members of the Nemetschek Group,

Tekla® and other leading software vendors joined forces to launch a global

program to help promote Open BIM collaboration workflows throughout the AEC

industry.

2.1.3.2 gbXML

The Green Building XML is an open schema intended to facilitate the assessment

of the energy analysis in buildings. The schema has been developed to transfer

information needed for preliminary energy analysis of building envelopes, zones,

and mechanical equipment simulation.

gbXML was created by Green Building Studio (GBS) in 1999 with funding from the

California Energy Commission and Pacific Gas and Electric, and it was released

soon thereafter. Originally it was developed as data schema format used by

GeoPraxis in Green Building Studio (currently owned by Autodesk) and its main

scope was to be a simplified data model towards exchanging the necessary data

to energy analysis tools (e.g. DOE-2, EnergyPlus, etc.). However, it quickly

became the standard schema for building information exchange and was adopted

by major BIM authoring and analysis software vendors. Recently (2008), GBS

was acquired by Autodesk, and gbXML was spun-off into its own non-profit

organization called the Open Green Building XML Schema, publicly available at


April 2012 36 CERTH

www.gbXML.org. This organization now includes a board of directors consisting of

more than 11 software companies, all of whom are key stakeholders in gbXML.

To further promote the data exchange via the use of gbXML data format, several

workshops and live webinars are organized by the gbXML.org, whereas several

dissemination material such as quarterly e-newsletters, an active website and

forum, and much more are used to increase the stakeholders awareness about

the schema evolution.

An overview of the schema is illustrated in the following Figure.

Figure 7: Overview of the gbXML schema (version v5.0)


April 2012 37 CERTH

The gbXML schema is a tree-like hierarchy of data placeholders. The latest

version of the schema (version 5.0) contains over 400 elements and attributes.

The transfer of elements and the information related to a construction product is

performed by the software tools with export and import functions. The gbXML

schema contains several types of data related to building geometry (i.e.

rectangular and/or planar geometry is supported), properties and attributes

related to the building envelope (e.g. wall u-values, window emissivity values,

etc.). It can entail schedules for building, lighting and occupancy, weather data,

shading properties, zone and HVAC system-related data, and much more.

The schema allows to incorporate a full set of data related to building information

that can be later on utilized by software tools to perform a wide variety of

analysis including:

� Whole building energy use & costs

� Water use & costs (indoor and outdoor)

� Carbon emissions

� Heating and cooling load analysis

� Renewable energy

� HVAC equipment sizing

� Lighting analysis

� Computational Fluid Dynamics (CFD) analysis

The application of gbXML deployed by Green Building Studio Inc. is currently

focused on the energy simulation domain. Users can upload a well-formatted

gbXML file to get a quick summary of simulation results from DOE-2 simulation

analysis engine based on contextually related assumptions. It is also possible to

get input files for EnergyPlus and EQuest from GBS after the simulation runs.

As the use of gbXML becomes more widespread, tools which utilize the schema

(currently over 40 software tools use it world-wide) must continually improve

their use of it in all stages of a building lifecycle from its design, to operation

maintenance and demolition phases. Though gbXML is not an official standard

recognized by any standards agency, it has become a defacto standard in the AEC

industry due to its wide adoption by energy analysis tools and corresponding

major architectural and design suites developed by Autodesk, Bentley,

Graphisoft, Google Sketchup, etc.

A list of major applications that support the standard can be found at

http://www.gbxml.org/software.php.


April 2012 38 CERTH

The following section concludes on the strengths and limitations of the two

standards reported in the previous paragraphs, whereas a short discussion is

made for the future of BIM in the AEC industry.

2.1.3.3 Comparison of IFC and gbXML

Market surveys and research in the AEC industry indicate that BIM related

modelling tools and standards are widely used by end-users, but do not fully

exploit the full potential of the building-related processes as they were illustrated

as part of the Virtual Building Modelling in Section 2.1.2 “Overview of the BIM

Paradigm”. The IFC and gbXML are the two dominant and well-established

information structures in the AEC industry, focusing on improving the information

sharing across stakeholders during the whole life-cycle of a building.

On one hand, IFC is the industry de facto standard that adopts a holistic approach

to represent an entire building project from the requirements phase, building

commissioning and construction to building operation. On the other hand, the

gbXML has been widely used in energy simulation tools developed by commercial

software vendors and has the ability to carry additional metadata to the static

building information models (BIM). Moreover, efforts have been made recently to

extend the applicability of the gbXML also to other simulations such as lighting

control [11].

IFC adopts a comprehensive “top-down” data schema and exhibits potential

benefits in its highly organized and relational data representation. In contrast, the

“bottom-up” gbXML schema focuses mostly on energy related building aspects. It

is simpler and easier to understand which facilitates quicker implementation of

schema extension for different design purposes.

In terms of geometry, the generic approach of IFC has the ability to represent

any shape of building geometry, while gbXML only accepts rectangular shape,

which is however enough for energy simulation. gbXML presents certain

limitations compared to IFC (e.g. limited detail in geometric boundaries, etc.),

however these are not of significant important when used in simulation engines.

The “top-down” approach of IFC yields in a relative complex data representation

and constitutes to the creation of large data files. This approach can trace back all

the semantic changes and in general has the ability to maintain semantic

integrity. However, it is very complex to program and be implemented in

software, essentially increasing the efforts of software vendors when new

releases of the schema are announced.

On the contrary, gbXML is flexible and a relatively straightforward data schema,

as it adopts the “bottom-up” approach. This approach has proven successful in


April 2012 39 CERTH

offering web-based simulation service (notably Green Building Studio) for the

industry. As aforementioned, gbXML consists of a very specific set of definitions

and data requirements focusing primarily on energy analysis. Its geometric

requirements deal only with spatial volumes and thermal zones with relatively

simple polygonal boundary surfaces. Thus, the analytical model needs accurate

yet simplified geometry properties and attributes. As building models continue to

gain in complexity, gbXML schema shall exhibit the ability to accurately capture

the intent of complex designs. Moreover, as gbXML’s utility is increased,

supporting documentation needs to be provided as well.

Concluding, both gbXML and IFC offer a powerful means of interoperability and

access to BIM data. The future efforts towards improving BIM interoperability

standards will benefit from previous technology. IFC standards rely significantly

on the EXPRESS language and the work focused on standardising the CAD

geometry exchange (the ISO 10303 - STEP Standard). gbXML and other

standards are benefiting from the existing broad technology support for XML

schemas for defining domain specific documents. BIM interoperability

specifications and formats remain in their early infancy, thus the construction

sector provides a significant challenge in the scope of the knowledge that needs

to be unambiguously defined, represented, agreed upon and eventually shared

among stakeholders. Long-term challenges will be to effectively elaborate existing

standards in order to support different construction activities and processes

depending on the level of details needed by each stakeholder and the phase of

the building lifecycle.

The enhancement of the BIM in the near future will further foster the deployment

of new paradigms for analyzing and improving the tools used for building

performance analysis and simulation.

The following sections describe in detail the current deployment of BIM-related

information that is used for the generation of models capable of analyzing and

predicting the performance of a construction product. Focus is given mainly to the

topics that will be addressed by Adapt4EE project, namely building energy

performance simulation models, occupancy and business models as well as

agent-based modelling techniques employed in the building performance

simulation domain.


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2.2 Building Performance Simulation

2.2.1 Definition and Importance of Simulation

Simulation is one of the most important tools available in our world. The

modelling and prediction of an outcome based on what happened in the past or

on current trends is critical to success in many fields, from economics, space

exploration, space simulation and planning to electronic circuit design, fire

fighting, evacuation planning and the design, construction and operation of

buildings.

Simulation, as a term, encompasses a number of different but similar notations,

in which “simulation” refers to a schematic description of a system, theory or

phenomenon that could represent the real world by a computer program. In

respect to the AEC context, only recently architects and engineers have turned

into computerize two/three dimensional or computer-aided design software

(CAD). One of the critical aspects during the design of a building is the

assumptions about how the building will be used and operated during its

commissioning phase.

Buildings entail complex information related to its descriptive data (e.g. building

envelope, walls, roofs, spaces, equipment, etc.) and the interactions of the

physical space with the environment and the building occupants. Designers in

order to understand how energy is used in space, the modelling of complex

interactions among building entities such as heat, light and moisture as well as

the underlying human presence and movement are key factors in evaluating

critical building performance issues, such as human comfort and productivity,

energy efficiency, code compliance and carbon emissions.

The following sections outline several simulation models that exist in the

literature towards providing new opportunities and functionalities to end-users

(architects, designers, engineers) into design practise. The main focus is given to

the simulation models that affect the overall energy performance of a building in

respect to the most critical factors affecting the building energy and

environmental performance.

2.2.2 Building Energy Performance Simulation Models

The dominant Building Information Models and respective tools capture three core

construction performance and behaviour aspects, that is:

a) Thermal load calculation: the thermal peak load analysis of

buildings (heating, cooling) based on which HVAC optimization can

be performed [12]. The early development of models was principally


April 2012 41 CERTH

aimed at predicting the internal temperature conditions in the context of

defining heating and cooling requirements. Interest was accelerated by the

recognition of the need for the rational use of energy and related interest in

solar energy. In this area three general trends are identified:

i. The development of programs for simulating the thermal behaviour

of whole building structures, containing detailed models of the

different thermal processes within a building

ii. The development of models for the simulation of specific building

elements (e.g. Trombe walls, solar collectors) generally

implemented in larger programs such as TRNSYS.

iii. The reduction of more complex models to 'simplified' models more

easily used, for instance, in studying the performance of control

systems.

b) Air-Flow Models: applying fluid dynamic and other models to

simulate airflow on the interior and exterior building spaces [20].

Apart from radiative and conductive transfers, building heat is also

transferred by convection. This involves both the heat transferred from air

to fabric as well as heat transferred by air movement. The latter includes

both the movement of air within a space or zone and movement between

zones. Air-flow models embedded in thermal models are usually very

primitive, such as simple user-defined constant mass flows. However,

advances in fluid dynamics together with improved computational power

have enabled the development of methods for predicting complex flow

fields. The International Energy Agency, Annex 30 [19], identified the

following three principal categories of model distinguished by level of

complexity:

i. Computational fluid dynamics: (CFD) involves the simultaneous

solution of the continuity equations for mass, momentum and

energy. The flow domain is divided into many small control

volumes and the discretised equations are solved iteratively for

each control volume and for the domain as a whole. A turbulence

model is required to account for the energy dampening effects of

viscosity. CFD is a very powerful tool for predicting the three-

dimensional air distribution within a space and is particularly useful

for complex situations. However, CFD requires powerful computing

facilities, specialist software and expert users and, is in

consequence, relatively expensive in both time and resources. This


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type of model also requires considerable, detailed input data much

of which is not available at the early stages of a design project. For

this reason it is mainly used at the detailed design stage to provide

fine-tuning or additional confidence in a proposed design, based on

simpler methods.

ii. Network models: These models address the flows between zones

in a building and between zones and outside air. They do so by

representing flows in the form of a network with the driving

potential being the pressures generated by the wind, temperature

difference and any installed mechanical ventilation system.

iii. Simplified models: These are essentially simplifications of the

network models. In most cases they treat the building as a single

zone and are generally used for predicting overall building

infiltration losses.

c) Interior lighting and acoustics simulation: analyzing factors influencing

the interior lighting and acoustic conditions of buildings towards predicting

the interior lighting levels (e.g. daylight level) and acoustics (e.g.

reverberation time) ([13], [17]).

Although recent market evidence reveals the continuously growing demand for

integration of structural design and analysis programs into building information

models, the actual business practice demonstrates limited BIM model tie-ins that

can dynamically and seamlessly integrate HVAC-related design programs (such as

load calculation programs, pipe and duct sizing programs, building energy

modelling/analysis programs such as Trace 700, DOE-2, EnergyPlus, Blast, etc.).

Very few companies have fully integrated either facilities operations and

management programs or daylighting and illumination simulation and design

programs (such as Superlite, LUMEN, or Radiance, AutoLUX, Autodesk

Lightscape, etc) into BIM models.

In general, existing energy performance modelling and simulation approaches

focus on the structural behaviour of buildings and its relation and response to

specific environmental conditions, failing to capture the driving factor of energy

consumption: the occupants. The available modelling methods and systems do

not deal with activities performed by occupants or with the resulting utilization of

space and movement through space. Due to the complexity of the problem with

capturing user preferences and activities engineers and existing simulation and

design tools tend to eliminate the influence of users as far as possible to optimize

building performance [21] eventually leading to assumptions about average user


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preferences and behaviours. This results not only in rough and imprecise

architectural designs in terms of energy performance of constructions during

future operation, but also in fully automated systems without interaction, poor

performance and low end user acceptance.

2.2.3 Occupancy Modelling and Simulation

2.2.3.1 Survey on existing occupancy modelling algorithms for

building energy efficiency assessment and evaluation

Energy consumption in enterprise buildings is a major source of carbon emissions

and is highly dependent on human presence and behaviour in such environments

([21], [23]).

As of today, various strategies and methods have been proposed to improve the

energy efficiency of commercial and home buildings that consider various

environmental factors including occupancy modelling ([24]-[28]). However,

energy use or waste due to human behaviour in the spatio-temporal domain is

not yet fully investigated in the literature.

In the past years, the principle algorithm for occupant presence modelling and

simulation was the diversity profiles [29]. Abushakra et al. ([29]) proposed a

well-established method that represents occupancy in a building via a time-

variation model, which is described through schedules and diversity factors

([30]). These profiles represent the combined behaviour of all occupants. For

instance, a diversity profile describes the presence of occupants and the

corresponding energy loads stemming from utility demands. Daily or yearly

schedules can be estimated using onsite survey or through individual experience.

Then, these schedules can be applied to building spaces with similar

characteristics for calculating the energy consumption due to the impact of

human presence in internal heat gains and cooling loads. In addition, diversity

factors were proposed to correct average heat gain estimations from the

aforementioned schedules, but in general they can not elucidate the stochastic

variations of building occupancy in the spatio-temporal domain. Overall, diversity

profiles offer a cost effective “black-box” modelling approach of the average

occupancy. However they fail to capture many of the underlying relations

between features and critical evidence affecting occupancy variations. For

instance, diversity profiles have failed to sufficiently capture dependencies of

occupancy patterns with overall environmental conditions ([25]) or temporal

variations ([31]).

Authors in [25] presented a sub-hourly occupancy control model in order to

propose a framework for whole-building energy simulation that can assess the


April 2012 44 CERTH

impact of short term occupancy variations and other environmental factors. By

using a suite of occupancy-based predictive models in ESP-r tool savings in

energy requirements have been reliably predicted. Moreover, the research on

sub-hourly occupancy schedules lead to effectively predict monthly peak power

demands on whole building. In a more recent study [32] on modelling the human

presence, authors suggested a probabilistic model for estimating occupancy in

buildings using networks of occupancy sensors. This approach as well as similar

methodologies ([21], [28]) overcome the limitations of simpler occupation

estimation methods but do not directly examine the occupancy modelling in the

spatio-temporal domain with correlation among data from different sensor

networks that exist or could be integrated in the enterprise buildings.

To cope with occupancy dynamics and human presence in time and space, Wang

et al. [35] proposed a probabilistic method to estimate the occupancy schedule in

a single person office. The proposed method assumes that building occupancy

and vacancy intervals during working hours are independent and sequential

random variables and models the durations of presence and absence during

business hours with exponentially distributed random variables. The coefficients

are estimated through measurement data, whereas indicative time-dependent

parameters such as arrivals and departures in the single office are modelled with

normal distributions towards analyzing and simulating the occupant pattern in

enterprise buildings. The specific approach addressed single person offices which

is not always the case in real life situations. Furthermore, intermediate periods of

presence and absence during the working day were treated as exponential

distributions with a constant coefficient over the day. This hypothesis was

confirmed in the case of absence but not in the case of presence.

A more comprehensive occupancy model was proposed by Zimmerman ([22]) for

the aim of improving the building control system (lighting, heating and cooling

system), which investigated the modelling of user activities over time taking into

account user groups, their roles in functional units and the tasks that they may

perform (Figure 8).


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Figure 8: Models used by Zimmermann ([22]) for occupancy modelling

Going one step further in building occupancy simulation, Tabak ([36]) presented

a sophisticated framework for simulating the human behaviour in buildings for

any given organization. In his study, the activities performed in office-based

organizations were thoroughly investigated and a taxonomy of tasks executed by

building occupants as well as to analyze the factors (individuals, organizational)

that influence the interactions occurred between individuals (e.g. performing

primary activities such as attend a business meeting, give a presentation, etc).

A similar approach for generating fictional occupancy in buildings was proposed

recently by authors in [37]. A hybrid approach was proposed to produce more

realistic patterns of human behaviour in buildings under design, in which

information found in statistical occupancy schedules was combined with optional

parameters supplied by the user in the form of personas attributes (e.g.

arrival/departure times per occupant, probabilities for office meetings, offsite

break, etc). Moreover, in a recent study from Shen et al. ([38]), a framework is

introduced, namely Building Information Modelling-based user activity simulation

and evaluation method (UASEM), whose ultimate goal is to conduct pre-

occupancy evaluation of buildings under design and to provide via user activity

simulation better understanding of the design solutions in terms of space layout

utilization. A snapshot of the UASEM method in action is illustrated in Figure 9.


April 2012 46 CERTH

Figure 9: Interface for user activity specification and occupancy scheduling used in UASEM

approach

2.2.3.2 Taxonomy of occupancy models used for building performance simulation

The previous section focused on a literature review on algorithms and methods

used for incorporating occupancy models that can be used by building energy

simulation tools. In general, the approaches used in SoA regarding occupancy can

be categorized to i) stochastic approaches, ii) agent-based algorithms and iii)

algorithms that combine both approaches in order to improve the accuracy of the

occupancy models for generating fictional occupancy schedules used later on in

building energy simulation tools. Next paragraphs provide several algorithms that

belong to each of the aforementioned categories, targeting on analyzing the main

characteristics and features used in each approach.

Stochastic approaches

A stochastic approach for generating occupancy models that can be used by

building energy simulation tools is adopted by the authors in [39]. The basic idea

behind the model is that building occupancy is straightforward result of occupant

movement processes, which occur among the spaces inside and outside the

building. For occupant movement simulation, a first-order homogeneous Markov

chain technique was selected ([40]). The proposed model has a two-level

hierarchical structure consisting of a basic module named movement process and

a high-level module named events.


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A simulation of the Markov chain process generates the step-by-step locations of

the occupants for the first level, where the second level is used to specify the

transition probabilities of the Markov chain in specific periods of time.

Figure 10: Schematic model used by authors in [39]

One of the limitations of the approach presented is the lack of real-world data for

validating the stochastic model. A more elaborated simulation system for

occupants’ movements in an instructional building is presented in [41]. Occupants

can be of different types (e.g. student, stuff) and have different types of

movement (e.g. class schedule, working hours). The movement data is generated

using the Monte Carlo simulation and given probability distributions. Two different

process models are used: i) one for activity scheduling simulation based in

activity-based model and another ii) for movement simulation, which is based on

a pedestrian movement model described in [42]. The simulation and visualization

system consists of methods for i) simulation of both activity scheduling and

occupants’ movement and visualization tools that can ii) demonstrate occupant

movements in both 2D and 3D and performance evaluation tools that can iii)

calculate multi-attribute of space performance.

Agent-based approaches

Agent-based approaches have recently been used widely in the building

simulation analysis and performance. For instance, the work presented in [43]

tries to provide support for building energy optimization calculations, by using

human and device agents to explore current trends in energy consumption and

management of a university test bed building. It attempts to maintain occupant

comfort while reducing building energy consumption with the use of reactive and

predictive control strategies. Finally, occupant agents are motivated by simulation

feedback to accept more energy conscious scheduling through multi-agent

negotiations. The incorporation of the agent-based control in simulation engines

demonstrated a 17% potential energy saving while maintaining a high level of

occupant comfort. Moreover, in [22] Zimmerman describes static and dynamic

models of individual users and user activities in an office environment in relation


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to building control systems. The models are based on a well-structured general

model of five different domains of building entities, including user activities.

These domains are the user activity, functional unit, control, service and building,

as illustrated in Figure 11.

Figure 11: Agent type structures used by Zimmerman ([22]) to support building occupancy analysis and simulation

The agent-based modelling approach is composed of the static and the dynamic

models. The static model defines agents, subagents and communication channels

between agents. The dynamic models describe the dynamic behaviour of the

agents and the messages they can interchange. Models for specific simulation

projects are refinements of these general models. For experimental evaluation a

case study was presented to validate the effectiveness of the occupancy

simulation approach followed. The real scenario uses a University building with

different types of occupants like students, faculty member or visitors and agent-

based entities are employed towards predicting the energy performance of the

building. Heating and cooling energy simulation results indicated that even when

irregular occupancy models occur, tangible results can be demonstrated in terms

of energy consumption prediction (actual usage and predicted usage).

Combined approaches (stochastic & agent-based)

A very recent novel stochastic agent-based model of occupancy dynamics in a

building with an arbitrary number of zones and occupants is proposed by authors

in [44]. The proposed model, named Multiple Modules (MuMo) model, decides the

location of an agent at a given time through a set of rules specified by a number

of modules. The modules are designed to maintain a Markov-like property of the

agent dynamics so that the location of an agent at a given time depends on its


April 2012 49 CERTH

location in the previous time. Simulation of this model produces a time-series of

each occupant’s location, which can then be collected to generate time-series of

zone-level occupancy. The information needed to specify the inputs to the model

can be obtained from survey of occupants, measured sensor data or a

combination of the two. The predictions of the model have been compared

against measured occupancy data in commercial buildings for three distinct

scenarios: single-occupant single-zone, multi-occupant single- zone and multi-

occupant multi-zone. For the single-occupant single-zone scenario, the data used

was the same as in [46] and the results were compared and were found to be

quite good. For the other two scenarios, data gathered in a building of the

University of Florida were used for validation (Figure 12).

Figure 12: Floor plan of the University of Florida Building used by authors in [44] for the validation and verification of the energy prediction of the MuMo model.

By using the building space layout, the multi-occupant single-zone and multi-

occupant multi-zone scenarios were evaluated. It was found through comparison

with measured data (actual usage) that MuMo model predicts certain variables

(e.g. mean occupancy) with high accuracy. The proposed agent-based model can

be used in conjunction with building performance simulation tools, while the

graphical model is more suitable for real-time applications, such as occupancy

estimation with noisy sensor measurements. However, the graphical model

introduced can capture only spatial correlations on occupancy. This means that

the statistical relationship among occupancy at distinct times (temporal

correlation) is lost and a future study is proposed to retain these temporal

dependencies.

Finally, a novel behaviour simulation method is presented in [37]. The method is

described as “schedule-calibrated” in the sense that it needs input of actually

recorded activities of real occupants in a building, and then it can be used to

generate synthetic schedules while striving to reproduce typical patterns of

behaviour. The method focuses mainly in the attributes of the type of task,

duration and number of participants. The approach for creating the synthetic

schedules is composed of four steps: population of a set of histograms using real


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occupant schedules; the smoothing of those histograms; the normalization of the

smoothed histograms; and finally the extraction of attribute values.

2.2.3.3 Conclusions on occupancy modelling and simulation

The findings of the literature review indicate that accurate analysis, prediction

and simulation of occupant behaviour in the early design phases of a construction

product could significantly improve the predicted energy performance of the

building during its commissioning phase.

Recently, several efforts have been made by the research community towards

delivering detailed occupancy models that cope with both occupant’s presence

and movement with occupant’s control actions (behaviour) into a single data

model. These models present significant limitations and weaknesses as they rely

on several assumptions [124] that do not fully explore occupancy modelling in

respect to key aspects such as the effective correlation of the organization to be

“housed” in the building under design with its occupant’s behaviour.

As far as contemporary research or existing technological tools ([47]-[48]) is

concerned there is no enterprise modelling (or simulation systems) which fully

exploit/address the actual effect of occupants and their respective

actions/behaviour in their working environments. For instance, in the field of

occupancy behaviour, modelling research is mainly focused on control-oriented

user behaviour, i.e. the interaction between the occupants and environmental

controls, like windows, lights and heating systems ([22], [27]) not taking into

account additional types of enterprise utilities.

Concluding, it should be pointed out that due to dynamic nature of the occupancy

in buildings, the construction of mathematical models that are appropriate on one

hand for reliable building occupancy estimation and on the other hand for real

time occupancy predictions remains a challenging research problem. Future

research should aim to deliver occupancy models that take into account i) the

spatial layout of the building under design (BIM model) and the ii) information

related to the organization to be “housed” in the building, fully exploiting the

critical business processes and related occupant behaviour patterns in order to

further reconcile the differences between the energy analysis of “real” and

“simulated” buildings.


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2.3 Multi-Agent based modelling and Simulation

2.3.1 An agent-based approach to modelling & simulation of

building performance

Agent-based Modelling and Simulation (ABMS) is one approach to representing

dynamic complex systems through autonomous computation, and to solve social

simulation and optimization problems by observing emergent behavioural

patterns. It has its origins in discrete event simulation, genetic algorithms and

cellular automata. This approach to representing the world is different from the

traditional approaches, which have been either purely theoretical, based on

mathematical or logical models and the use of deduction, or purely pragmatic,

based on reality monitoring and the use of induction as means to derive facts in a

bottom-up reasoning. The agent paradigm for representing reality is an

alternative that surmounts some obstacles in understanding the functioning of a

complex physical system, due to a model “grounded in reality” with more precise

rules of change. The major contribution of Multi-Agent Systems is modelling of

the entities in the real world, e.g. people, devices, buildings and other physical

systems, and their interactions with the environment. This allows one to approach

complexity as the result of interaction between relatively simple ecosystem

components, with a predictable behaviour.

Agent-based models reflect the complexity of emerging global systems as

consequence of the local interactions of the members of a population. An agent is

a computational entity that can be viewed as perceiving and acting upon its

environment autonomously, having its actions or behaviour depending on its own

behavioural rules, internal state such as acquired experience, and its own

objective functions for evaluation of situations.

Simulation permits improved understanding of complex physical and social

systems through controlled computational experiments. Agent- based modelling

can improve the accuracy of complex system forecasting and provide insights into

the factors and variables that are the primary drivers of emergent behaviours in

complex systems with multiple inter-dependencies.

Particularly for the AEC industry, agent-based modelling and simulation represent

attractive alternatives for studying emergent properties resulting from the

interaction of the building components and the use of the building in daily

activities.

Agents use the principles of Object-Oriented Modelling; classes and methods

represent agent types and agent behaviours. Software objects are encapsulated

(and usually named) pieces of software code. Software agents are software


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objects with some degree of control over their own state and their own execution,

such as, by having an own thread of control. Also, while software objects are

fixed, reactive, i.e. always execute when invoked, as predicted by their

behavioural rules, and have static relationships with one another. Software

agents are dynamic, are requested (not invoked), may not necessarily execute

when requested, or conforming to the predicted behavioural rule, and may not

have fixed relationships with one another.

In fact, agents can be viewed as self-directed objects endowed with an execution

thread, with the capability of choosing actions autonomously based on their

perceived conditions of the environment.

The majority of popular Agent-based Modelling frameworks are based on OO

principles, and some even use extensions of UML for high level agent and system

specification. Most of the agent frameworks also are accessible through an

Application Programming Interface (or API) and application layer, which allows

one to programmatically access, i.e. communicate with the agents.

2.3.2 Literature review on taxonomy of Agents

The agent is a concept for modelling distributed applications, and the ability to

work cooperatively in teams. Jennings [69] has identified the fundamental

characteristics each agent must have:

• Situated: the agent receives sensory input from its environment and can

perform actions which change the environment the environment in some

way.

• Autonomous (ability to act autonomously): it should be able to act

without intervention from humans or other agents and have control over

its own actions and inner state. It should also have a high-level

representation of behaviour.

• Flexible, i.e. it should be responsive, proactive and social, combining pro-

active and reactive behavioural characteristics. Agents should perceive the

environment and respond to changes that occur in it. Agents should

exhibit goal- directed behaviour and interact with other agents and

humans in order to complete their own problem solving and help others

with their activities. Flexibility endows the agent with the capability to

learn and adapt its behaviour over time.

• Identifiable and specifiable: having a set of characteristics and rules

governing its behaviours and decision-making capability.


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Figure 13: Generic Agent Overview

An Agent is defined as an evolvable system that perceives its environment

through sensors and acts upon that environment through actuators.

The Environment in which agents interact has several attributes:

� partially observable

� stochastic (i.e. non-deterministic)

� sequential (not episodic)

� dynamic (not static)

� continuous (not discrete)

� multi-agent (not single-agent); within this scenario, the agents can be

Situated or non-situated; Self-interested / Socially Interested;

Cooperative or Competitive

Agents can rely on built-in knowledge (typical for semi-autonomous, rational

agents) and/or maintain current state of environment from sensors (specific for

autonomous agents)

The most relevant types of agents for the Adapt4EE scenario need to exhibit the

following attributes:

� Reactive: able to react to changes in its environment

� Goal-aware: able to define objectives and to achieve them

� Utility-based: able to measure the degree of progress towards its goal(s)

� Rational: able to reason

� Enhanced with Learning ability

An agent is a rational agent when it employs


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� the performance measure defining the agent’s degree of success

� the perceived situation, the collection of all the facts perceived by the

agent through its sensors

� the agent’s knowledge of the environment

� the actions that the agent can perform

Rational agents can maintain an internal state (for storing their memory of the

environment). They could be goal-aware (i.e. have one or more objectives that

they seek to achieve), utility-aware (i.e., they have some indication of the

expected benefits for themselves of performing each action) and their main

behaviour is that for each possible situation, they attempt to maximize the utility,

based on sensors and built-in knowledge.

Even more powerful than rational agents are agents endowed with ability to adapt

to their environment. These agents (rational or not) attempt to maximize a

performance criterion which measures their success with respect to attaining a

given objective. To some extent, they can be seen as learning a specific objective

function which optimize the value of the performance criterion.

When agents are rational and adaptive, their behaviour is viewed as intelligent

and able to solve complex problems.

The following section provides an overview of the existing agent platforms

relevant for the tasks required by Adapt4EE project. The tools and technologies

selected for the literature review has been based on the definition of a set of

desired criteria and characteristics that are relevant for the scope and objectives

of the Adapt4EE platform.

2.3.3 A literature review on Agent Platforms relevant for

simulation and analysis in Adapt4EE project

2.3.3.1 Evaluation criteria and characteristics for Agent

Platforms

Prior analyzing the available state-of-the-art agent frameworks that exist in the

literature, a list of criteria and requirements has been defined, and should be

taken into account for their assessment and evaluation. Next paragraphs outline

these criteria, whereas a list of most relevant agent-based platforms that could

be used in Adapt4EE is presented in Section 2.3.3.2.

• Generic Features of the Agent Platform

o State Persistency Model

o Portability

o Library Support

o Flexibility/Configurability/Extensibility of existing components


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o Adaptability/Evolvability of agent behaviour through learning,

reasoning, coordination

• Support for Agent Modelling Lifecycle:

o Terminology/Business Glossaries and Models specification

o Requirements & Design/Modelling

o Development/Integration with an IDE

o Execution: interactive configuration, debugging, visualization of results

o Deployment&(Re)Configuration

o Maintenance

o Monitoring

o Simulation Recording, Analysis & Reporting

o Billing & Charging, i.e. using a Cost/Pricing model

• Implementation Languages supported:

o Implementation language of the platform

o Development languages (functionality for the platform users)

o Maintenance (functionality for the platform administrators)

• Ease of technology adoption

o Ease of use: How easy it is to learn the technology using the

documentation? Is documentation comprehensive, current and easy to

navigate?

o Usage/Licensing model, and its impact on functional extensibility

o Cost

o Open Standards and Protocols supported, Compliance with Open

Standards

o Standard services provided

o Performance for implementing intelligent algorithms

o Interoperability with other similarity tools

o Popularity/Size of Applications base

• Technical Architecture Evaluation criteria:

• Scalability: Can the technology scale-up to realistic problem sizes? Does

the technology support a distributed deployment/simulation over multiple

machines?

• Ease of modification/distribution: How easy is it to modify the

solution/ code to extend and/or re-factor the functionality to other agent

collections, or to physically locate agents in different locations? Does the

technology support location transparency?

• Architecture flexibility: Does the technology promote a particular

architectural style, such as shared blackboard, distributed memory, or


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publish/subscribe? Or does the technology provide the flexibility of

configuring the system architecture to the problem domain?

• Mobility: Does the technology support mobile computation?

• Inter-agent communication mechanisms: Does the technology

provide flexible inter-agent communication mechanisms (including

synchronous, asynchronous, multicast)? Does the system support event-

driven architectures?

• Robustness of implementation: Can the technology pass main tests

during development & testing? If some mechanisms are prone to failure,

are there backup strategies to ensure warning and suspension, saving and

resuming of simulation at a later point, or in a different location

2.3.3.2 General Purpose Agent Simulation platforms

During the literature review performed within Adapt4EE, the following agent

modelling and execution platforms have been considered for analysis:

• CHAP (http://chap.almende.com)

• AgentScape (agentscape.org)

• Cougaar (cougaar.org)

• JADE (jade.tilab.com)

• JACK (agent-software.com/products/jack/index.html)

The candidate technologies include a few state-of-the-art primary technologies

supporting agent modelling and execution (Table 3), and a few social interaction

simulation platforms (Table 4). The criteria for the selection and comparison of

the existing technologies have been presented in the previous section. For each

tool and technology investigated, the platform provider is included with additional

features (OS supported, languages for the development, cost/licensing, etc.) that

can play important role to the final selection of a suitable solution for Adapt4EE.

Table 3: Agent-based modelling and execution platforms overview

AgentScape CHAP Cougaar JADE JACK

URL agentscape.org

chap.almende.com

cougaar.org jade.tilab.com agent-software.com/products/jack/index.html

Provider/

Vendor

TU Delft & DECIS Lab, Delft, NL

Almende BV, Rotterdam, NL

Open Source designed and developed within DARPA research programs (UltraLog)

Telecom Italia AOS NA, Annapolis, Maryland, US


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Platform Type

Agent Platform Agent Model, Platform,

Component Library

Agent Platform

Agent Plaform Agent Plaform

Platforms Supported

Windows, Linux, Unix/Solaris (any with JavaVM)



Windows,Linux, Unix (all with JavaVM)

Windows, Macintosh, Unix (all with JavaVM)

Features Supports inter-agent communication, visualization, agent discovery, mobility of code, security

Open source;

Can run in the cloud, and on mobile devices; library of AI components;

Visual development

Distributed Agent Architecture; Cognitive Agent Architecture; BDI Agents; Ontologies for reasoning support

FIPA, FIPA-ACL compliant;

Graphical tools;

Agent mobility;

Remote GUI;

Run on mobile devices;

Integrated with JESS reasoning engine

FIPA-compliant;

Light-weight, efficient, cross-platform foundation;

Graphical Agent Development Tools

Languages Java C/C++, Java, Javascript

Java Java Java

Cost/ Licensing Model

Open Source Open Source License

Open Source; modified version of OSI BSD license

Open Source Agent Platform/LGPL

Commercial

Services Directory Services, Servlet, Chat, Visualization

Demos, visualizations

Agent Management, Directory Services, Visualization

Model Management for scenario definition and scenario execution;

Time management infrastructure with guaranteed repeatability of simulations, regardless of computation location;

Visualisation/development of 2D visualisations of model execution

Ease of use Easy; examples & documentation

Moderate Moderate Moderate Moderate


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Communication Protocols

RMI, SOAP REST, JSON/JSON-RPC

RMI, JMS HTTP, IIOP UDP/IP (proprietary), support for CORBA, RMI, etc.

Popularity Known in academic community (10 yrs)

Not widely adopted yet

(2 yrs)

Applied in industrial projects

Known in academic, applied, industrial projects (>5 yrs)

Known in academic community and industrial projects (air traffic management, mobile robots, automation systems, etc)

Pro/Cons Pro: Open source, reasonable control of existing code; used for few commercial projects; active development

Pro: Developed by Almende, open source, good control of features/functionality;

used in commercial & applied projects; active development

Cons: not a large user base

Pro: Scalable distributed agent architecture used in logistics projects for defense domain

Pro: Open source, complete agent-based framework, active development Cons: Used mainly for research projects and frameworks

Pro: Lightweight, efficient cross-platform framework, transparent inter-agent communications, graphical agent development tools

Cons: Non-open source but includes an academic licensing policy

In addition to the aforementioned core agent-based platforms [65], there exist

also some agent-based frameworks that focus on social interaction & simulations,

which are outlined in the following Table 4.

• MASON (http://cs.gmu.edu/~eclab/projects/mason/)

• NetLogo (http://ccl.northwestern.edu/netlogo/)

• RePast (http://repast.sourceforge.net/)

• StarLogo (http://education.mit.edu/starlogo/)

• Swarm (swarm.org)

Table 4: Agent-based platforms on social interaction & simulation overview.

MASON NetLogo RePast StarLogo Swarm

URL http://cs.gmu.edu/~eclab/projects/mas

http://ccl.northwestern.edu/netlogo/

repast.sourceforge.net

http://education.mit.edu/starlogo/

swarm.org


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on/

Provider/

Vendor

Center for Social

Complexity, George

Mason University, USA

North Western University, Evanston, Illinois, US

Argonne National Laboratory

MIT Media Lab and

MIT Teacher Education Program

Univ. of Michigan, USA,

Santa Fe Institute /

SWARM Development

Group, USA

Platform Type

Fast discrete-event multiagent simulation library core - foundation for large custom-purpose Java simulations

Multi-Agent Modelling & Simulation Platform

Agent Platform(s)

Programmable modeling environment

Agent Plaform

Platforms Supported

Windows, Linux, MacOS X with OpenGL support

Windows, Linux, MacOS X

.NET, Java (any with JavaVM)

MacOS, Windows, Linuxm

Linux, Windows, and Macintos, IRIX, Solaris

Features Model library and an optional suite of visualization tools in 2D and 3D;

Fast, portable, small

multi-agent programmable modelling environment; large library of sample models;

multiplatform complexity modelling and simulation environment

Family of Agent-based Modelling & Simulation Platforms;

agent templates and examples;

automated Monte Carlo simulation framework

programmable modelling environment for exploring the behaviours of decentralized systems, such as bird flocks, traffic jams, and ant colonies

Object-oriented framework for agent-based models. Provides tools for implementing, observing and conducting experiments on ABMs.

Languages Java Java C++, Java Java Java, Python, Objective C, C#.Net

Cost/ Licensing Model

Open Source, AFL/Academic Free License v3.0

Download available free of charge

Open Source, New BSD License

Available online as OpenStarLogo (no open source)

Open Source under GNU general Public Liicense

Services Modelling, simulation, visualization in 2D and 3D.

Multi-agent programmable modeling environment

Suite of gent-based modeling and simulation platforms

Use in large computing clusters supporting visual model

Agent-based simulation language

Object-oriented agent programming and development

Graphical interfaces for agent-


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construction, flowcharts, etc

based modelling

Ease of use Moderate Easy to learn and use

Moderate Easy to use and learn

Moderate

Popularity Relatively known

Popular (>10 yrs)

Relatively known

Known in many academic and applied projects

Popular; active development (15 yrs)

Pro/Cons It is open source, can run on popular IDEs.

The framework is fast, portable and models are independent from visualization framework.

The framework is easily extendable from Java, Scala and other JVM languages.

A large library of sample models is available.

Even if the tool is easy to learn, it can not scale well to larger and complex models.

The Repast framework allows the use the environment via a hybrid approach, i.e. via IDE or as a standalone library.

This flexibility may require more knowledge to use than other types of IDEs. However, such tools tend to scale most effectively in large and complex modelling frameworks.

StarLogo is a shareware / freeware system which is accompanied by a variety of demonstration models.

Instead of object-oriented modelling approach, the framework models are programmed procedurally. Thus, models do not benefit in abstraction shared between the agent-based and object-oriented paradigms.

Swarm offers as Repast integrated GIS functionality (for spatial modelling) and it is intended for development of complex adaptive systems based on multi-agent simulations.

One possible drawback found in the literature survey was that the tool has a steep learning curve from the modeller perspective [64].

It should be noticed that the two agent-based categories presented above are not

mutually exclusive, but for the sake of simplification the platforms included in the

above tables, based on their primary/dominating features.

2.3.4 Literature review on agent-based simulation tools and

engines for the Building Energy Performance domain

One major objective of energy simulation in building design is the possibility to

compare architectural design alternatives based on energy usage and

environmental qualities such as thermal comfort. The result of an energy

performance simulation provides a prediction of the absolute or relative energy

values.

The traditional energy performance simulation tools have not been agent-based

([33]). Typical examples of building energy oriented performance tools are the


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following DOE2/eQuest, Energy Plus/BLAST, SPARK, COMIS, etc. Some of them

are used in the context of building design, as support for modeling or in

conjunction with validation of these simulation results, e.g. BESTEST (Building

Energy Simulation Test).

The majority of the tools are used for Computational Fluid Dynamic (CFD)

simulations, while other tools are used for temporal usage estimation. From this

last category, eQuest is able to provide a detailed summary of the hour-by-hour

energy operation of the building.

In the recent period, the modelling of more dynamic spatial properties and

dynamic aspects of occupancy and environment received increasing attention

(see [34]).

The focus of simulation is on several complex aspects:

• Simulation of building physics and building services - lightning, heating,

cooling, ventilation, insulation, etc;

• Human simulation of the indoor environment;

• Simulation for external environmental conditions: earthquakes, flooding,

fire propagation, etc;

• Simulation of enterprise-oriented or civil-oriented energy capture and

conversion in buildings with solar and geothermal energy capture; and

• Simulation of building-related properties and situations, including design

practice, e.g. designing Energy-Efficient buildings.

A few example of the tools used for simulation purposes are: ShowFlow & XJ

technologies; Rockwell Automation; SIMULE-Planner; AutoMOD; PMC-Kanban

Simulator; Program Portfolio Simulator; Asprova Scheduler; 3D simulator tool-

kits; Wolverine Software-SLX; OPNET; OMNET++; NIIST; NS-2; NS-3; ATDI ICS;

Qualnet and Dymola.

A few application areas in which simulation tools can be applied for the building

domain have been identified (see [34]). Several agent-based tools, most of them

general purpose agent modelling and/or simulation tools, have been applied in

the past to the building domain and/or to analyse energy performance. These

tools are listed in the sub-sections below.

2.3.4.1 StarLogo

StarLogo ([56]) is a programmable modelling environment for exploring the

workings of decentralized systems - systems that are organized without an

organizer, coordinated without a coordinator. With StarLogo, you can model (and


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gain insights into) many real-life phenomena, such as bird flocks, traffic jams, ant

colonies, and market economies.

2.3.4.2 AnyLogic

AnyLogic ([70]) is commercial multi-paradigm simulation tool, which brings

together Discrete Event modelling of system dynamics, and agent-based

modelling within one common modelling language and development environment.

AnyLogic set of primitives and library objects allows one to model manufacturing

and logistics, business processes, human resources, consumers' and patients'

behaviour, as well as the environment (the “background”) in their natural

interaction. Models can dynamically read and write data to spreadsheets or

databases during a simulation run, as well as to output charts dynamically.

The agent framework supports discrete event simulation, continuous time

simulation, system dynamics and agent-based simulation. The object-oriented

model design paradigm supported by AnyLogic enables modular and incremental

construction of large models, and can deploy both Java applets and applications.

External programs can be invoked from within AnyLogic models, to facilitate

dynamic communication of information.

AnyLogic contains a large library of models, containing many practical examples

of models that have been developed for a diverse range of applications including:

the study of social, urban and ecosystem dynamics, e.g. a predator prey system;

IT and Communication Networks, e.g. the placement of cellular phone base

stations; the location of emergency services and call centres; and pedestrian

dynamics. The models are configurable and the tool supports visual 2D and 3D

simulation. However, the source code of the examples and/or documentation of

these models are only available after acquiring of a commercial license.

2.3.4.3 Brahms

Brahms ([71]) is a multi-agent language and agent-based simulation tool running

atop a Java virtual machine. The Brahms VM can run in simulation mode or in

real-time mode, which allows us to use Brahms also as a MAS development

environment. It can be used freely for research purposes.

2.3.4.4 Cormas

Cormas ([72]) is an agent-based simulation platform based on a SmallTalk-based

programming environment. It allows the development of agent-based applications

in the Smalltalk Object-Oriented language. Cormas pre-defined entities are

represented as Smalltalk generic classes from which, by specialisation and

refinement, users can create specific entities for their own model.


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The software has mostly been applied to management of natural resources,

namely studying the interaction of human societies with the Earth's eco-system.

The tool is closely related with Mimosa meta-modelling and simulation platform,

which provides the means to specify any kind of modelling and simulation

formalisms and is able compose and run any models written in these formalisms.

Cormas has a library of previously implemented models, some of which are

theoretical, other didactical (showcasing functionality), or coupled models, i.e.

integrated with other tools (geographic information systems, data base, other

models). The models range from the management of a renewable resource

(water, wild fauna, tree and wood, soil and erosion, pasture, etc.), to the

economic exchanges of agricultural products, natural resources, and land-use

dynamics.

2.3.4.5 NetLogo

NetLogo [54] is a cross-platform multi-agent programmable modelling

environment and one of the most used agent simulation tools. NetLogo was

authored in 1999 at Northwestern University in US and is under continuous

development at the CCL, in the same group that produced StarLogoT. NetLogo is

a free academic tool, which also powers the HubNet participatory simulation

system.

2.3.4.6 Ps-I

PS-I ([73]) is an bio-inspired environment and a simulation language for running

agent-based simulations. Models are written using the standard Tcl/Tk scripting

language and a graphical interface can also be used.

2.3.4.7 SeSAm (Shell for Simulated Agent Systems)

SeSAm (Shell for Simulated Agent Systems, [74]) is a generic environment for

modelling and experimenting with agent-based simulation. SeSAm agents consist

of a body, which contains a set of state variables and a behaviour that is

implemented in form of UML-like diagram. Based on an extensive number of

primitive components, a user is able to design a simulation graphically without

knowing the syntax of a traditional programming language. It is written in Java

and it is freely downloadable.

2.3.4.8 Simio (Shell for Simulated Agent Systems)

Simio ([75]) is an industrial simulation tool which combines Object-Oriented

Simulation with Discrete Event, Continuous, and Agent-Based Simulation. Simio

implements several technical innovations to overcome the scalability and difficulty


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of use problems common in most Objecti-Oriented (OO) products. While Simio's

standard library of objects includes some sophisticated manufacturing objects

(like accumulating conveyors that merge), it remains general purpose to

effectively model many common applications. The included full 3D animation

produces compelling animation. The ability to modify and define intelligent

objects using process graphs without programming makes simulation software

easier for decision-makers to use.

2.3.4.9 SimWalk

SimWalk ([76]) is an agent-based pedestrian simulation software developed by

Savannah Simulation in cooperation with the ETH Zurich, in Switzerland. With

SimWalk you can model and simulate the behaviour of large aggregations of

people in places such as shopping centres, railway stations, bridges, airports and

so on.


April 2012 65 CERTH

2.4 Semantic-enabled technologies for building

management

2.4.1 Semantically enhanced building & device models

2.4.1.1 Semantic modelling of buildings

Human interpreter is the primary target for technical drawings used to represent

buildings. Common BIM models like IFC, gbXML mentioned in previous chapter

are used to exchange the information about buildings between computers. In

some level these ensure interoperability between systems that support the same

common model. Semantic technology however, can provide a solution fully

interoperable between different users and computer systems. Here are some

reasons why:

• Distribution, generation of partial models, versioning and consistency

issues that arise in heterogeneous collaboration environments can be

addressed by using Semantic Web representations that have built-in

networking capabilities such as the Resource Description Framework (RDF)

• Open world modelling methods are more feasible for handling

heterogeneous and often incomplete information typically found in

collaboration in the building and construction sector than traditional

closed-world modelling approaches in cases where incomplete information

has to be dealt with.

• Complex building product models can be maintained more easily than

currently practiced in standardization bodies when using methods from the

knowledge representation systems domain that make use of strict logical

semantics.

• Different user needs for the interpretation of the model can be addressed.

The owner of the future building wants to get a general idea about the

overall character of a design and some of its key performances (what is it

going to cost, how long will it take to complete the building?). A domain

expert such as a building energy performance expert needs detailed

information about the geometrical and topological arrangements of

building elements and their materials as well as their attributes used in the

design. A construction worker needs information about the concrete size of

walls, the type of concrete or brick that has to be used and the point in

time his or her work is required within the overall schedule of the building

erection.


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Using semantic technologies in building domain usually means combining existing

technologies from the building domain with existing technologies from the

semantic web domain. The next section will outline some selected approaches.

IFC2X3 ontology

One of the activities of Multi Media Labs at Ghent University, Belgium, in the area

of BIM, is the transformation of IFC data from EXPRESS schema into an Web

Ontology Language (OWL) [78]. In this transformation process, every EXPRESS

element that has a direct equivalent in OWL is mapped onto this equivalent. More

specifically, for each ENTITY element in EXPRESS a corresponding OWL class is

generated, EXPRESS attributes are converted into the appropriate OWL

properties, etc.

The experiences with the transformation has showed that fully automatic

conversion of EXPRESS schemas into an OWL ontology will probably never be

reached, due to certain types of semantic information that do not have a direct

equivalent in OWL, such as WHERE rule constraints and procedural FUNCTION

calls. However, this mainly concerns more advanced rule and function

information, which is typically described in the semantic web field using rule

languages instead of relying on the standard reasoning capabilities of OWL.

There is also available the IFC-to-RDF conversion service and the SPARQL

endpoint, which generates the RDF instances that can be obtained from existing

BIM applications.

ifcOWL ontology

Another approach to transform the IFC EXPRESS schemas to OWL is described at

[79]. Author tries to leverage the power of computational tools towards exploring

the hidden semantics of multi-dimensional building product models. The resulting

ifcOWL model covers both the stringent definition of the concepts and their

relations amongst each other as well as the instantiated occurrences of these

concepts that have been derived from models generated by legacy CA(A)D and

building product modelling tools. Different configuration approaches for the

generation of such models are being discussed and evaluated for their suitability

in different scenarios. On top of these models a range of operations for the

completion of common tasks in planning and building are being carried out to

demonstrate effects of using this approach in interoperability scenarios. The

fragment of the class hierarchy is illustrated in Figure 14.


April 2012 67 CERTH

Figure 14: An ontology-based representation of the IFC classes

BIM ontology for AEC in the pre-design phase

The paper [80] proposes the ontology-based solution to the AEC (Architecture,

Engineering, and Construction) pre-design phase. As the BIM has been applied to

AEC industry, the project life-cycle management requires the control over the

space objects and managing the information resources. Controlling and Managing

the space objects (building objects) is a complex work, because each object has

many properties, such as size, height, material, cost, and position etc. But these

functions might be core competency of BIM. Authors propose the ontology as the

support for control of the space objects in the BIM.

Figure 15: A conceptual ontology to outline predesign phases of a building using concepts of the AEC industry.


April 2012 68 CERTH

The taxonomy of the building components (such as floor, wall or roof) and their

interrelations is presented as the OWL ontology. Then, according to the client

needs, the ontology model is created and constrained, e.g. construction cost, size

of the rooms, number of rest rooms, etc. According to the relations and

constraints, the number, positions and number of space objects is controlled.

Finally, the ontology model is converted to the BIM-based tool. The illustration of

the taxonomy fraction is at Figure 15.

Architectural Reconstruction of 3D buildings

Authors in [81] presented an ongoing research, which aims at combining

geometrical analysis of point clouds and semantic rules to detect 3D building

objects. Firstly by applying a previous semantic formalization investigation, they

propose a classification of related knowledge as definition, partial knowledge and

ambiguous knowledge to facilitate the understanding and design. Secondly an

empirical implementation is conducted on a simplified building prototype

complying with the IFC standard. The generation of empirical knowledge rules is

revealed and semantic scopes are addressed both in the bottom up manner along

the line of geometry - topology - semantic, and a vice versa top down manner.

The implementation is based on the Protégé platform with Semantic Web Rule

Language (SWRL).

a.s.Catch system

a.s.Catch system [82] proposes the sketch-based approach when using the floor

plan repository for queries. This enables the user of the system to sketch a

schematic abstraction of a floor plan and search for floor plans that are

structurally similar. They also propose the use of a visual query language, and a

semantic structure. An algorithm extracts the semantic structure sketched and

compares the structure of the sketch with that of those from the floor plan

repository.

Semantic Web Energy Efficiency Project (SWEEP)

Another approach is introduced by Semantic Web Energy Efficiency Project

(SWEEP). Project SWEEP [83] is a knowledge base developed on semantic web

principles to build a rich picture of the built environment. A picture composed of

detailed building models. They start with a semantic version of GreenBuildingXML

(gbXML) and add FOAF, CIM and other relevant ontologies. Then they load all this

into a semantic Web server and RDF store (the ARC). The information gathered is

further extended by thermal images. The RDF store contains about 120000

sentences (triples) about the building and is queriable using SPARQL.


April 2012 69 CERTH

2.4.1.2 Semantic modelling of devices

These days there exists a quite huge amount of ontologies for description of

devices, which were designed as the result of institutional scientific work or R&D

EU projects. In the most cases, the models have the very similar information

basis and the differences are, of course, application or domain specific. Due to the

number of several existing models and approaches, this section will outline only

few of the selected examples, which might be most relevant for the Adapt4EE

approach. In Adapt4EE it is also important to focus the energy-related behaviour

of devices. Generally, any semantic model can be easily extended with the

additional energy-related information depending on the purpose and usage of the

device. The examples were selected to outline the basic modelling approaches or

to address also the concrete energy-related information.

FIPA device ontology

The device ontology introduced by FIPA (Foundation of Physical Agents) in 2001

was one of the very first compact device descriptions. The FIPA device ontology

developed the modelling pattern for addressing several device capabilities, which

is now commonly used in the most of the models. The FIPA ontology was

designed to be used by agents when communicating about the various properties

of devices. For example agent can ask another agent whether the device has

enough capabilities to handle the certain task. The frame-based representation of

the ontology is illustrated on Figure 16.

SEIPF ontology

The Semantic Energy Information Publishing Framework (SEIPF) [84] was

designed to publish the power consumption information and other appliance

properties, in a machine understandable format in the smart home environment.

The energy-related information are modelled using the energy profiles

represented in ontology (so-called E.P. ontology). The SEIPF approach should

serve as the framework providing metering and visualization of energy

information to realize the statistics and analysis of the energy data.

The core of E.P. ontology contains the device profile representing the device,

which has attached more consumption profiles related to the concrete device

states (e.g. switched-on/off). The consumption profile represents the power

consumed by device in concrete state. The consumption is described by

nominal/real power consumption value, the unit (e.g. Watt) and the associated

device state. For accessing the information in the machine understandable

semantic format, SEIPF uses the Domotic OSGi Gateway (Dog) [85] that is able

to expose different domotic networks as a single, technology neutral automation

system. Dog uses the DogOnt ontology [86] to model devices and house


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environment. Dog provides ability to control different devices installed in a house

environment and to query different device properties ranging from location to

current operating state. E.P. ontology was created as an extension of the DogOnt.

The example of the device power consumption profile is illustrated on Figure 17.

Figure 16: Device ontology introduced by FIPA (Foundation for Intelligent Physical Agent) in 2001.

Figure 17: Overview of the device consumption profile based on the ontology defined in

Semantic Energy Information Publishing Framework by Bonino et al.


April 2012 71 CERTH

DEHEMS ontology

DEHEMS (Digital Environment Home Energy Management System project) is EU

funded initiative [87] to influence energy consumption behaviour of household by

providing the advice on efficient energy consumption and visibility to their energy

consumption data. One of the outcomes of the DEHEMS project is the ontology

for home energy management domain. The ontology encodes knowledge of home

appliances, their energy efficiency, and knowledge of energy saving

strategies/tips. The ontology was developed with focus on energy efficiency

characteristics of the appliances as much as possible to provide and rich

knowledge representation for reasoning tools to not only reason about short term

energy efficiency of an appliance but also provide a long term operational aspects

of the appliance energy consumption (for example a washing machine that

consumes less energy per cycle but consume more water may not be an energy

efficient machine in the long term).

Figure 18: Ontology overview used in tbe DEHEMS European project, including conceptual alignment with SUMO upper level ontology schema.


April 2012 72 CERTH

The ontology development takes into account energy efficiency rating/labelling

provided by ENERGY STAR [88] and European Energy Efficiency Labels [89].

ENERGY STAR is a US Environmental Protection Agency and US Department of

Energy backed program helping businesses and individuals to protect the

environment by using more energy efficient appliances, machines and energy

saving strategies. ENERGY STAR rating provides a more detailed view of energy

efficiency of the appliances. EU label define energy efficiency of washing machine

on a scale from A to G, with A most efficient and G least efficient.

The ontology is aligned with SUMO upper level ontology [90] to allow the

knowledge sharing and information retrieval in the common form. The illustration

example of DEHEMS ontology hierarchy is on Figure 18.

SESAME ontology

The project SESAME [91] uses semantic modelling and reasoning to support

home owners and building managers in saving energy and in optimizing their

energy costs while maintaining their preferred quality of living. A semantic layer

has been designed as a technical solution that integrates smart metering,

building automation and policy-based reasoning in order to offer an energy-

optimization capability for the energy consumer and provider.

SESAME uses an ontology-based modelling approach to describe an energy-aware

home and the relationships between the objects and actors within the control

scenario.

The ontologies provide a hierarchy of concepts to model the automation domain

and the energy domain. The ontology includes a number of general concepts such

as resident, location, and concepts in the automation and in the energy domain,

such as device, tariff or energy usage profile. The devices can be the appliance,

sensor or UI device. Device model contains the set of properties, e.g.

consumption per hour, peek power, the switch on/off status but also the required

state “to be switched on/off”. The central function-level concept in the SESAME

ontology is the configuration class, which has two subclasses: activity

(automation activity) and energy policy. A configuration connects appliance,

sensor and UI device into a joint task. The configuration can provide regulation of

different types, e.g. regulation on time, occupancy of location, threshold value.

For this purpose configuration includes properties including thresholds and

scheduled times.

The knowledge base contains the system-level rules, which complement the

definition of automation activities and energy policies in the ontology. The

system-level rules specify how the information from the knowledgebase is used to


April 2012 73 CERTH

reason about the changes on the appliances state. Energy management rules are

executed after automation rules to verify the automation decision based on

energy constraints. The example of the system-level rule working with the

ontology information looks as follows:

Activity(?a), Sensor(?s), regulatesOnThreshold(?a,?s),

usesAppliance(?a,?d), hasReading(?s,?r), isSwitchedOn(?d,false),

hasThresholdSwitchOn(?a,?t), lessThanOrEqual(?r,?t) ->

IsToBeSwitchedOn(?d, true)

Semantic Sensor Network Ontology (SSN)

The W3C Semantic Sensor Network Incubator Group provides a formal OWL DL

ontology for modelling sensor devices (and their capabilities), systems and

processes [93]. The ontology is based around concepts of systems, processes,

and observations. It supports the description of the physical and processing

structure of sensors. Sensors are not constrained to physical sensing devices:

rather a sensor is anything that can estimate or calculate the value of a

phenomenon, so a device or computational process or combination could play the

role of a sensor.

In general, the sensors observe the stimuli to infer information about

environmental properties and construct features of interest. The SSN ontology

revolves around the central Stimulus-Sensor-Observation pattern, which acts as

the upper core-level of the Semantic Sensor Network ontology. The pattern is

developed following the principle of minimal ontological commitments to make it

reusable for a variety of application areas. SSN ontology is aligned to the ultra

light version of the DOLCE foundational ontology [94].

Several conceptual modules build on the pattern to cover key sensor concepts,

such as: basic skeleton, devices, measuring capabilities and constraints, energy

consumption, data, processes, operating restrictions, platforms, deployment and

systems containing the sensors. The ontology does not include a hierarchy of

sensor types; these definitions are left for domain experts, and for example could

be a simple hierarchy or a more complex set of definitions based on the workings

of the sensors. The modules contain the classes and properties that can be used

to represent particular aspects of a sensor or its observations: for example,

sensors, observations, features of interest, the process of sensing (i.e.: how a

sensor operates and observes), how sensors are deployed or attached to

platforms, the measuring capabilities of sensors, as well as their environmental,

and survival properties of sensors in particular environments.


April 2012 74 CERTH

The SSN ontology can be extended with the energy module defining the energy

management aspects, such as battery lifetime or operating power range. The

illustration of SSN ontology energy extension is illustrated on Figure 19.

Figure 19: Overview of ontology defined by W3C Semantic Sensor Network Incubator

Group for modelling sensor devices, systems and processes.

HYDRA ontology

One of the goals of the HYDRA project was to develop a middleware solution for

networked embedded systems in ambient environments. The main output of the

project is the middleware, which enables to connect various heterogeneous

devices providing different services and with different capabilities. It combines

the use of ontologies with semantic web services, supporting thus true ambient

intelligence for ubiquitous networked devices. The ontologies in HYDRA are

mainly focused to model the devices. The models are used for both static

information storage and also complex query answering purposes.

The HYDRA device ontology represents the concepts describing device related

information, which can be used in both design and runtime. The basic ontology is

composed of several partial models representing specific device information. The

initial device ontology structure was extended from the FIPA device ontology

specification [95] and the initial device taxonomy was adopted and extended from


April 2012 75 CERTH

AMIGO project vocabularies for device descriptions [96] (within the Amigo project

there was also created quite wide set of device ontologies dedicated mostly for

the smart home automation). The major ontology concepts used in HYDRA

ontologies were the device and the service. The core ontology contains the

taxonomy of various device types and the basic device description including

model and manufacturer information. Device services are modelled in the terms

of operation names, inputs and outputs. The services are also organized into the

taxonomy. The services are the basic executable and functionality units in

HYDRA. To enrich the device and service description, the models can be

annotated several additional information, such as various capabilities, software

and hardware features, quality of service or security properties.

The model of device was also extended with the energy profile, which served as

the device energy consumption information represented by the energy

classification, energy operation, the related energy mode and state. The

illustration example of the HYDRA device ontology energy module is in Figure 19.

Figure 20: Overview of the ontology used in the Hydra project for the device ontology

closely related to energy consumption information

2.4.2 Ontology Management

Generally, in the area of ontology development, the most crucial parts of the

semantic models management are: how ontologies will be maintained; where and

how they will be stored and accessed; how the information will be retrieved; and

how complex processing is needed. The answers to these questions have direct


April 2012 76 CERTH

influence on the selection of the formal language; reasoning and storage engine;

and the editing tools.

Today’s implementations and technologies in the area of semantic technologies

are focused on the development of the semantic databases – the triplestores. The

triplestores enable end-to-end functionality for storing and accessing/querying

the ontologies, but they also implement their own reasoning mechanisms

optimized for the implementation details of the storage. On the other side, the

ontologies or semantic meta-models have to be created and maintained

somehow.

In this section, an overview is provided for the available triplestores and ontology

editors.

2.4.2.1 Semantic Triplestores

A wide variety of triple stores is available nowadays for storage of semantic

information in RDF and/or OWL. The semantic triple stores are often integrated

into a framework that provides querying interfaces and data maintenance

capabilities. In the following paragraphs, we provide a survey of some of the

most known triple stores together with a short description of technology used,

functionality provided, advances, and licensing policies.

JENA

Jena [97] is a popular and frequently used Java framework for building Semantic

Web applications. It provides a programmatic environment for RDF, RDFS and

OWL, SPARQL and includes a rule-based inference engine. Jena includes an API

for both RDF and OWL, together with capabilities of reading and writing RDF in

RDF/XML, N3 and N-Triples. The built-in repository enables in-memory and

persistent storage of semantic data, together with the reasoning and inference by

means of SPARQL query engine. The SDB and TDB are two subsystems for

persisting RDF and OWL data in Jena. TDB is focused on the data access by

means of Jena APIs, while SDB is for the RDF storage and query specifically to

support SPARQL. Java framework is available under open source license.

Sesame

Sesame [92] is a popular open source Java framework for storage, inference and

querying of RDF data. It can be used as a database for RDF and RDF Schema, or

as a Java library for applications that need to work with RDF internally. Sesame is

internally organised into a modular and layered architecture, where the semantic

data repository and the respective Storage And Inference Layer (SAIL) interacts

with functional modules such as the SeRQL, RQL and RDQL query engines, the


April 2012 77 CERTH

admin module, and RDF export. Access to these functional modules is available

through Sesame's Access APIs, consisting of two separate parts: the Repository

API and the Graph API. The Repository API provides high-level access to Sesame

repositories, such as querying, storing of RDF files, extracting RDF, etc. The

Graph API provides more fine-grained support for RDF manipulation, such as

adding and removing individual statements, and creation of small RDF models

directly from code.

AllegroGraph

AllegroGraph RDFStore [98] is a high-performance persistent RDF graph

database. It is capable to handle billions of triples in a good performance.

AllegroGraph supports SPARQL, RDFS++, and Prolog reasoning from numerous

client applications. The AllegroGraph features include effective database

replication mechanisms, pre-indexing of triples, full text and free text indexing,

powerful query analyzer, transaction processing, and many others. AllegroGraph

is written in Common Lisp; the clients are available for all main platforms (Java,

Python, Perl, etc.). Licensing of AllegroGraph depends on the allowed capacity of

the store and ranges from free version (< 50 mil. triplets) to commercial

enterprise version.

BigOWLIM / SwiftOWLIM

SwiftOWLIM and BigOWLIM [99] are variants of the OWLIM family of semantic

repositories, called also as RDF database management systems. This framework

provides native RDF engines, implemented in Java and compliant with Sesame

and Jena, robust support for the semantics of RDFS, OWL Horst, OWL 2 QL and

OWL 2 RL, high scalability, loading and query evaluation performance. It is

declared by vendors that the SwiftOWLIM is the fastest semantic repository in the

World: it supports non-trivial inference with tens of millions of statements on

contemporary desktop hardware. The advantage of BigOWLIM is its scalability

and multi-user query performance. OWLIM framework is based on the Triple

Reasoning and Rule Entailment Engine (TRREE) of Ontotext. It is implemented in

Java and packaged as a Storage and Inference Layer (SAIL) for the Sesame RDF

database. SwiftOWLIM is available for free for any purpose; BigOWLIM is provided

free of charge for research, evaluation and development purposes. For

commercial use, licenses of BigOWLIM are offered at a flat pricing model, where

the price is proportional to the capacity of the servers on which the engine will be

installed.


April 2012 78 CERTH

BigData

BigData [100] is a horizontally-scaled, general purpose storage mechanism for

ordered data (B+Trees), designed to operate on either a single server or a cluster

of commodity hardware. Bigdata uses dynamically partitioned key-range shards

and thus it may be deployed on 10s, 100s, or even thousands of machines and

new capacity may be added incrementally without requiring the full reload of all

data. The Bigdata RDF database supports RDFS and OWL Lite reasoning, high-

level query (SPARQL), and datum level provenance. Bigdata is written in Java and

is freely available under an open-source license (GPL v2).

Mulgara

The Mulgara Semantic Store [101] is an open source, massively scalable,

transaction-safe, purpose-built database for the storage and retrieval of RDF,

written in Java. It provides the RMI or embedded data access, JRDF and REST

programming interfaces to the semantic repository. Data access and querying is

allowed by means of TQL or SPARQL. Mulgara is licensed under the Open

Software License v3.0.

OntoBroker / Ontoprise

OntoBroker [102] is a deductive, object-oriented database system that has

originally been developed as a research prototype at the AIFB Karlsruhe as part

of the Semantic Web initiative. As Ontobroker had matured, it went commercial

and is now available through Ontoprise. The OntoBroker is an implementation of

highly scalable Semantic Web middleware. It supports all W3C Semantic Web

recommendations such as RDF(S), OWL, SPARQL, RIF and ObjectLogic. The new

ObjectLogic is best of breed of RDF, OWL and F-Logic concerning the expressive

power and evaluation performance. The Ontobroker semantic framework includes

the RDF triple store, query and inference engine. It is well integrated into the

general OntoBroker suite, allowing close interaction between the other supported

knowledge representation formats of OWL and F-logic.

Virtuoso

OpenLink Virtuoso is a SQL-ORDBMS and Web Application Server hybrid that

provides SQL, XML, and RDF data management in a single multithreaded server

process. Triple store access is available via SPARQL, SIMILE Semantic Bank API,

ODBC, GRDDL, JDBC, ADO.NET, XMLA, WebDAV, and Virtuoso/PL (SQL Stored

Procedure Language). Virtouso [103] is also an OWL Reasoner, which supports a

subset of OWL subclass or sub property relations. It also includes a Live SPARQL

Query Service Endpoint in all installations. The product is developed in C

language and is available in Open Source and Commercial editions.


April 2012 79 CERTH

2.4.2.2 Ontology Editors

There is lot of various ontology editors available on the market, such as Knoodl

[104], Neon Toolkit [105], Neologism [106], Ontoprise [107], Ontolingua [108],

SWOOP [109] and many more. For illustration, this section will briefly describe

the two selected ontology editors and management tools: Protégé-OWL editor

and TopBraid Composer. These two tools were selected because they are well

known, most commonly used, they have the strong support for several ontology-

management related tasks and they have strong community support.

Protégé

Protégé [110] is a free, open-source platform that provides a suite of tools to

construct domain models and knowledge-based applications with ontologies.

Protégé implements a rich set of knowledge-modelling structures and actions that

support the creation, visualization, and manipulation of ontologies in various

representation formats. Further, Protégé can be extended by way of a plug-in

architecture and a Java-based Application Programming Interface (API) for

building knowledge-based tools and applications.

The OWL ontologies are managed by the Protégé-OWL editor, which enables

users to perform the most of the task required for the ontology management,

such as: load and save OWL and RDF ontologies; edit and visualize classes,

properties, and SWRL rules; define logical class characteristics as OWL

expressions; execute reasoners such as description logic classifiers; or edit OWL

individuals for Semantic Web markup.

The Protégé architecture is easily configurable and extendable by wide scale of

various plug-ins to support several ontology-related tasks in areas of

visualization, annotation, working with databases, etc. It's tightly integrated with

Jena, but it is also possible to use any DIG-compliant reasoner, such as Pellet,

Fact++, RacerPro or KAON. The illustration screenshot of one of the basic views

is at Figure 21.


April 2012 80 CERTH

Figure 21: Snapshot of Protege platform for creating ontologies.

TopBraid composer

TopBraid Composer [111] is an enterprise-class modelling environment for

developing Semantic Web ontologies and building semantic applications. It is fully

compliant with W3C standards and offers wide support for developing, managing

and testing configurations of knowledge models and their instance knowledge

bases. In its basic free-of-charge variant, TopBraid Composer enables users to

load, edit and save RDF/XML, N3 and N-triples files; define ontologies using form-

based editors; create and execute SPARQL queries; create and execute SPARQL

rules (SPIN); constraint checking to validate user input (using SPARQL rules

(SPIN)); and merge and re-factor RDF data across different namespaces and data

sources.

TopBraid Composer is one of the leading industrial-strength ontology editors and

SPARQL tools available. It is thanks to its huge amount of tools and plug-ins

supporting many areas related to the ontology management, such as several

visualisation views of the ontologies, support for import/export to practically any

format/notation, working with RDF databases and existing inference engines (e.g.

Jena SDB/TDB, AllegroGraph, OWLIM, Sesame and others); work with different

reasoners and configure inference options; query relational databases in real

time; and many more.


April 2012 81 CERTH

As part of TopBraid Suite, Composer incorporates a flexible and extensible

framework with a published API for developing semantic client/server or browser-

based solutions that can integrate disparate applications and data sources. The

illustration screenshot of the standard ontology visualization tool is at Figure 22.

Figure 22: Ontology management and visualisation using the TopBraid composer software

2.4.3 Services Management of Heterogeneous Devices

2.4.3.1 Middleware for accessing heterogeneous devices

As service-oriented architecture (SOA) has becoming widely used for distributed

software, many efforts have been done to adopt this architecture for middleware

for embedded devices so that devices can be integrated easily into business

applications.

The concept of middleware, which can be defined as software that manages

connections between data produces and data consumers, evolved from the topical

area of distributed systems. Historically, middleware for distributed systems has

emphasized homogeneous computing platforms, i.e., all computing devices were

identical or of the same general device classification. Two of the fundamental

problems to overcome initially in this domain were a scalable naming schema and

the issue of time synchronization, especially with respect to total ordering of

events. The original application drivers for distributed systems were file sharing

and redundancy/replication. Common middleware architectures in this regard

include CORBA, DCOM, .NET, and the JAVA RMI.


April 2012 82 CERTH

The integration of smart devices like WSN, smart meters, embedded systems and

RFID systems into business applications is challenging, since each device type has

its own protocols and programming interfaces. Existing middleware simplifies

development of software which runs on smart items but not integration with

back-end systems.

The goal is to abstract from these differences and provide business applications

with a uniform way to communicate with smart items.

Available Tools

UPnP represents an early adoption of SoA for devices, which unfortunately with

the development of web service standard, it became incompatible.

European Union funded projects such as AMIGO, SIRENA, and SOCRADES have

developed frameworks for devices adopting web services. They rely on Device

Profile for Web Services (DPWS).

DPWS technology is becoming a standard web service for embedded systems,

addressing these problems:

� Discovery ensures that services offered by devices can announce themselves and be discovered by the consumers.

� Eventing ensures that interesting events are delivered to the interested application.

� Metadata Exchange describes what the service does and how services can be accessed. Thus it provides a better interoperability for heterogeneous platform.

� Security ensures that sensitive data is encrypted on the exchange process.

� Addressing describes how services can be addressed.

� Policy allows services to publish their policy on security, quality of service etc.

(SI) - Smart Items Services Infrastructure was developed to unify the

middleware architectures of two EU-funded projects.


April 2012 83 CERTH

Figure 23: Smart Items architecture of the unified middleware of accessing

heterogeneous devices

The architecture of this middleware is divided into two layers, a platform

independent and a platform dependent one. The platform dependent layer

consists of Message Handlers and Service Lifecycle Managers.

Message Handlers convert events generated by smart items of a specific platform

into platform independent events that can be picked by middleware components

and applications. On the other hand, Message Handlers convert generic

invocations of services running on smart items into the respective platform

specific mechanisms. Service Lifecycle Managers are responsible for deploying,

starting, and stopping services. The main components of the platform

independent layer are the Request Processor, the Service Mapper, and the

Service Repository which are presented in the subsequent sections.

Other well-known service discovery protocols:

� Simple Service Discovery Protocol (SSDP) as used in Universal Plug and

Play (UPnP)

� Service Location Protocol (SLP)

� Jini for Java objects.

� Salutation

� WS-Discovery (Web Services Dynamic Discovery)


April 2012 84 CERTH

2.4.3.2 Device Awareness middleware platforms

OSGi

The Open Services Gateway Initiative (OSGi) technology provides specifications

for home network gateways which coordinate many device technologies and

enable compound services across different networking technologies.

OSGi specifies only the API, not the underlying implementation, consequently,

OSGi gateway is platform independent. For a home network that consists of

several subnetworks, each of which has its own discovery technology, the OSGi

gateway can bridge higher-layer discovery protocols by importing services from

different discovery protocols and register them as generalized OSGi services, thus

allowing different discovery protocols to interact with one another.

KNX

KNX is a standardized (EN 50090, ISO/IEC 14543), OSI-based network

communications protocol for intelligent buildings. KNX is the successor to, and

convergence of, three previous standards: the European Home Systems Protocol

(EHS), BatiBUS, and the European Installation Bus (EIB). The KNX standard is

administered by the Konnex Association.

CORBA

CORBA is a standard of application middleware made by OMG, Object

Management Group. CORBA is the most widely used and deployed distributed

object technology. It is an open industry standard, providing an object-oriented

middleware spanning over heterogeneous platforms, (packet-) network protocols

and programming languages.

A large number of today's modern application servers are based on CORBA as

backbone of an enterprise's information service, used in the middle tier between

legacy systems at the backend and typically conventional Web servers at the

front-end.

CORBA specifies a set of horizontal and vertical services. Among the horizontal

services are:

� Naming and Trading Service for service registry and discovery,

� Event Service for pulling and pushing typed and untyped events,

� Transaction Service for coordinating distributed transactions across

multiple databases and legacy systems.

Kairos

Kairos is a middleware that implements abstraction of the node structure to allow

macroprogramming – but one still needs to program sensors.


April 2012 85 CERTH

In an existing approach, uniform invocation of services on embedded systems is

achieved with web service technology. Embedded systems are normally unable to

handle the overhead incurred by standard web services. Therefore, light-weight

web services are used which reduces the size of SOAP messages. As requests are

translated on the embedded system, all web service clients can directly invoke

such services without a special middleware. However, this approach is limited to

devices which are powerful enough to run a SOAP engine. Thus, it is not

applicable to RFID and most sensor node types.

2.4.4 Relevant European Projects

2.4.4.1 Hydra Project (LinkSmart Middleware)

Hydra has pioneered research into service-oriented architectures for networked

embedded devices based on a semantic model-driven approach. System

developers are provided with development tools for easily and securely

integrating heterogeneous physical devices into interoperable distributed

systems.

The Hydra project ended successfully on 31 December 2010. The software results

of the project have been published under the name LinkSmart middleware and

under the well-recognized and respected Lesser GNU Public License (LGPL). The

source code is freely available at sourceforge1.

The LinkSmart middleware allows developers to incorporate heterogeneous

physical devices into their applications by offering web service interfaces for

controlling any type of physical device irrespective of its network technology. The

middleware incorporates means for Device and Service Discovery, Semantic

Model Driven Architecture, P2P communication, and diagnostics. Hydra enabled

devices and services can be secure and trustworthy through distributed security

and social trust components of the middleware. Figure 24 shows an example of a

LinkSmart network consisting of various kinds of devices. Each device is

connected to the middleware by a set of software components, either deployed

on the device itself or – if the device is not powerful enough – on a gateway.

Once “LinkSmart enabled” device talk to each other over an overlay P2P network

(indicated by the yellow flashs).

1 http://sourceforge.net/projects/linksmart/


April 2012 86 CERTH

Backbone(IP-Network)

NM SM VM

LinkSmart - Midleware

NM SM VM


NM SM VM


WS (over IP)

WS

(ov

er IP

)

WS (over IP)

WS (over IP)

NM SM VM D2


D1 D3

LinkSmart Gateway

Non-LinkSmartEnabled Device




LinkSmart enabled Device

LinkSmart enabled Device

WS

WS

LinkSmart Bridge

(IP to WIFI)

WS

WSWSBT

WIFI

WS

WS (over WIFI)

Web Server UPnP

Figure 24: LinkSmart Middleware overview (HYDRA Project)

Partners FhG/FIT and TUK are members of the Hydra project and can freely

transfer their knowledge to the Adapt4EE project.

LinkSmart is used and further developed in a number of European Projects from

different application domains, reaching from healthcare over large-scale

enterprise applications to energy efficient buildings. In the course of these

projects LinkSmart itself will become more robust and developer tools will be a

great help for application developers.

Various kinds of LinkSmart device proxies are currently under development in the

different projects. These efforts are subject to constant assessment and

consolidation to foster a high degree of reusability for developers.

Further, semantic modelling is now diverging from generic models to more

application specific models, meeting the reoccurring requirements of specific

application domains.

LinkSmart is currently applied and further developed in the following European

Projects (of which the most relevant with regard to Adapt4EE will be described in

more detail below):


April 2012 87 CERTH

Table 5: Past and Ongoing European Projects using LinkSmart Middleware in various application domains

Application Domain

HealthCare Enterprise Energy Efficiency Security

Euro

pea

n P

roje

ct

• inCASA

(http://www.incas

a-project.eu)

• REACTION

(http://www.react

ion-project.eu)

• BEMO-COFRA

(http://www.be

mo-cofra.eu)

• Ebbits

(http://www.eb

bits-project.eu)

• Adapt4EE

(http://www.adapt4ee.

eu)

• ME3GAS

(http://www.me3gas.eu

/)

• SEAM4US

(http://seam4us.eu/)

• SEEMPubS

(http://seempubs.polito

.it/)

• BRIDGE

(http://www.bridgeproject

.eu)

• MASSIF

(http://www.massif-

project.eu/)

2.4.4.2 SEEMPubS (FP7)

Smart Energy Efficient Middleware for Public Spaces

SEEMPubs specifically addresses reduction in energy usage and CO2 footprint in

existing public buildings without significant construction works, by an intelligent

ICT-based energy consumption monitoring and managing.

Special attention is paid to historical buildings to avoid damage by extensive

retrofitting. SEEMPubS will create real-time energy-awareness services for all

users of the public space and combine awareness services with a community

portal.

SEEMPubS utilizes the LinkSmart middleware to integrate different kinds of

sensor/actuator technologies and Building Management Systems into a Building

Energy Management System. The LinkSmart device proxy approach is going to be

further developed to ease the integration of technology specific to energy efficient

building management.

In contrast to Adapt4EE, SEEMPubS aims at the operational and

retrofitting/refurbishment phases of the building lifecycle. Nevertheless, lessons

learned from device management approaches in SEEMPubS can be transferred to

Adapt4EE and taken further. E.g. methodologies for managing wireless sensor

networks with LinkSmart middleware can be applied in Adapt4EE.

Further, SEEMPubS aims at developing ontologies and context awareness

components for smart energy efficient buildings, modelling the application domain

and devices. As Adapt4EE aims for defining a common information model, the


April 2012 88 CERTH

modelling efforts of SEEMPubS play an important role in finding models that can

be applied during all phases of a building’s lifecycle.

FIT is involved in the SEEMPubS project and will foster technology and knowledge

transfer between both projects.

2.4.4.3 ebbits (FP7)

Ebbits does research in architecture, technologies and processes, which allow

businesses to semantically integrate the Internet of Things (IoT) into mainstream

enterprise systems and support interoperable end-to-end business applications. It

will provide semantic resolution to the Internet of Things and hence present a

new bridge between backend enterprise applications, people, services and the

physical world. The pilot application domains of ebbits are car manufacturing and

pork industry chain management. Ebbits tries to solve a broad range of large-

scale issues related to IoT architecture and enterprise integration.

Ebbits uses the LinkSmart middleware to create the connection between devices

and appliances and enterprise applications. Although ebbits does not specifically

deal with energy efficiency, the efforts in the field of device management should

be considered. Results will be directly integrated into LinkSmart, so LinkSmart

device management will become a robust and generic methodology for different

application domains.

FIT is coordinator of the ebbits project and will ensure knowledge transfer

regarding LinkSmart device management.

2.4.4.4 ME3GAS (Artemis Program)

The purpose of ME³GAS is to research and develop an energy-aware middleware

platform making it possible to network heterogeneous physical devices into a

service-oriented architecture. The middleware will hide the complexity of the

underlying device and communications technologies for application developers

and raise the level of programming abstraction to a web services layer and

provide necessary functionality and tools to add energy efficiency features to any

application. The ME³GAS project builds on the architecture developed in the FP6

project Hydra for embedded heterogeneous physical devices.

2.4.4.5 EnPROVE(FP7)

EnPROVE aims to develop a software model for predicting the energy

consumption of a specific building, with different scenarios implementing energy-

efficient technologies and control solutions based on actual measured

performance and usage data of the building itself. The EnPROVE software tools


April 2012 89 CERTH

will also assess how well alternative technologies supplying alternative energy can

work, thus supporting the decision maker to find an optimized set of energy-

efficient solutions. These results will be tailored to the actual building itself,

through automated measurements of the building usage and energy efficiency.

EnPROVE aims to develop tools, interoperable with existing CAD or Facility

Management tools, to model the energy consumption from monitored data,

predict the performance of alternative scenarios and support the decision maker

in finding the optimal point for the investment.

2.4.4.6 FIEMSER (FP7)

FIEMSER aims to develop an energy management system for existing and new

residential buildings (BEMS), keeping in focus the efficiency of the energy used

and the reduction of the global energy demand of the building. It is based on a

combination of sensors and actuators from traditional PLC-based BEMS systems,

connected via an OSGi+SOA REST architecture. FIEMSER also creates a “light”

family of devices that could be integrated on the system through wireless

connectivity links. FIEMSER relies on External Knowledge Sources to provide

information about the external building operation conditions (e.g., weather

forecast, dynamic energy prices, etc.)

2.4.4.7 CoBIs (FP6)

The CoBIsI (Collaborative Business Items) project aims to develop a new

approach to business processes involving physical entities such as goods and

tools in enterprise environments. An important goal of this project is to apply

recent advances in the area of sensor networks, in order to distribute business

logic functionality to "smart" physical entities thus reflecting what is actually

happening in the real world. (http://www.cobis-online.de/)

2.4.4.8 PROMISE (FP6)

The PROMISEII (Product Lifecycle Management and Information Tracking using

Smart Embedded Systems) project aims to leverage the potential of smart

embedded networked systems for product lifecycle management. By monitoring

the status and operational conditions of products, real-world data is collected to

support decisions for predictive maintenance, recycling, and product design

improvements. (http://www.promise.no/)

2.4.4.9 SaveEnergy Project (CIP)

SAVE ENERGY was a European Project that addressed the challenge of behaviour

transformation through the use of ICT (serious game and real time information)


April 2012 90 CERTH

as an enabler of energy efficiency in five Public building in five European cities –

Helsinki, Leiden, Lisbon, Luleå and Manchester. The SAVE ENERGY platform

comprises a local data collection platform to be installed in 5 different pilot

buildings and a central platform where the information from all the pilots is

consolidated, analysed and compared to energy efficiency models which will be

optimised along the project. The interaction with the users through real time

information or serious games can take place locally or remotely by using wireless

terminals or the Internet, namely the Web 2.0 portal, which will be used as a

social network tool for behaviour transformation.

The local platform shows the resource and device integrator part, which will

enable the interoperability of sensors, actuators and meters. This layer will

integrate into the central platform middleware which allows the integration of bus

systems, mobile and computer platforms. The user interface layer includes web

interfaces for computers and mobile devices, the gaming and simulation engines

and a business logic service.

Figure 25: SaveEnergy Project (CIP) architecture overview

The sensors measure energy usage, and other valuable data of the pilots and the

local gateway gathers all data from the each of the sensors and provides this to

the central platform. As all of the pilots use different integrators and suppliers,

they will probably do not have equal hardware with interoperable communication

protocols.

The central platform gathers the data from all the pilots in the project, analyses

the energy usage data, and makes the data accessible in a format that the clients

can read. The clients can either be a mobile telephone, a computer, or a screen

on the wall.


April 2012 91 CERTH

The innovative aspect of the proposed architecture is integration of an internet

layer, and the use of web 2.0 concepts, e.g. “component and service based

architectures” for the integration. architectures for building automation are still

predominantly based on process automation concepts, which were developed

before the Internet. A move to Internet inspired architectures will reduce vendor

specificity; increase interoperability of devices and through this will reduce cost

and increase adoption.

The local platform shows the resource and device integrator part, which will

enable the interoperability of sensors, actuators and meters. This layer will

integrate into the middleware, shown as “Internet” in the middle part of the

diagram, which allows – at IP level – the integration of bus systems, mobile and

computer platforms. The user interface layer, the third layer is shown on the right

side of Figure 25, including web representation for computers and mobile devices,

the gaming and simulation engines, and a business logic service.

The local part of the platform includes the network of sensors and actuators that

are connected to a controller that also receives the information of smart metering

and sends out information to the actuators. Besides electricity consumption, SAVE

ENERGY solution monitors temperature inside and outside the building, natural

light levels and intensity so that these parameters can be included in the

evaluation and correlation of energy consumptions. SAVE ENERGY will use

devices that can monitor energy consumptions in each plug individually. It is the

ideal tool to identify sources of waste in an office environment, in a low-cost

straightforward manner. Moreover, no permanent installations are required.

These devices also enable an automatic power cut through commands sent from

a PC, a PDA or a mobile-phone via wireless communications

SAVE ENERGY provides insights of the functionalities needed for energy efficient

open services architecture that supports energy service production, deliver and

use through savings. The open service architecture will help to create service

architectures for sustainable and distributed forms of user and demand-driven

ways to produce, deliver and use energy services.

2.4.4.10 AMIGO (FP6)

AMIGO middleware aims at enabling ambient intelligence within the networked

home environment by seamless integration of heterogeneous service

technologies. It also deals with service deployment using semantic descriptions of

services. The Amigo Open Source Software follows the paradigm of Service

Orientation, which allows developing software as services that are delivered and

consumed on demand. The benefit of this approach lies in the loose coupling of


April 2012 92 CERTH

the software components that make up an application. Discovery mechanisms can

be used for finding and selecting the functionality that a client is looking for.

Many protocols already exist in the area of Service Orientation. The Amigo project

supports a number of these important protocols for discovery and communication

in an interoperable way. This makes it possible for programmers to select the

protocol of their choice while they can still access the functionality of services that

are using different methods.

2.4.4.11 SOCRADES (FP6)

SOCRADES objective was to develop a design, execution and management

platform for next-generation industrial automation systems, exploiting the

Service Oriented Architecture paradigm both at the device and at the application

level.

SOCRADES has explored more intelligent generic middleware approaches. It

includes many features, e.g. service discovery, composition, and management

though.

The middleware technologies developed in this project were based on the

Service-Oriented Architecture (SOA) paradigm, encompass both wired and

wireless networking technologies, and provide open interfaces enabling

interoperability at the semantic level. A SOCRADES service is a software

component encapsulating device-specific functionality. This functionality is

advertised to the outside world, so as to be located and invoked by other

networked devices and/or applications without the latter being aware of how the

functionality is implemented.

2.4.4.12 eDiana (ARTEMIS)

eDiana (Embedded Systems for Energy Efficient Buildings) addresses the need of

achieving energy efficiency in buildings through innovative solutions based on

embedded systems.

The eDIANA Platform is a reference model-based architecture, implemented

through an open middleware including specifications, design methods, tools,

standards, and procedures for platform validation and verification. eDIANA

Platform enables the interoperability of heterogeneous devices at the Cell and

MacroCell levels, and it provide the hook to connect the building as a node in the

producer/consumer electrical grid.

Thus, eDIANA provide a Reference Architecture for a network of composable,

interoperable and layered embedded systems that will be instantiated to several

physical architectures. The eDIANA Platform realisations cope with a variable set


April 2012 93 CERTH

of location and building specific constraints, related with parameters such as

climate, Cell/MacroCell configuration (one to many, one to one etc), energy

regulations etc.

2.4.4.13 AIM (FP7)

AIM's main objective is to develop technologies for managing energy consumption

in domestic environments in real-time. Residential users administer their home

networks while functionalities are exposed as services to the outside network via

a gateway offering functions for policy management, device discovery, and

proactive configuration. AIM will provide a reconfigurable middleware to allow

uploading new functions to gateways, which can then be used by clients

2.4.4.14 SOFIA (ARTEMIS)

With the SOFIA project solutions the "embedded information" in the physical

world can be made available for smart services - connecting physical world with

information world. The project envisions the SOFIA environment, where

embedded systems (ES) connect, discover and enjoy personalized and

cooperating services operating on interoperable, heterogeneous data. Connection

at the lower level occurs through a SOFIA general overlay based on legacy

connectivity and communication protocols that can be seen as the basic

communication layer of the architecture. At a higher level of abstraction, each ES

can participate to one or more smart spaces. ESs participating to the same smart

space are connected through a second level overlay. From an application

viewpoint, ESs participating to a smart space overlay may provide services/data

to another smart space. SOFIA's goals encompass the middleware layer and go

up to user-interface layer.

2.4.4.15 Other Projects

RUNES, SM4ALL, EMMA, and POBICOS are also examples of EU-funded

projects that aim at middleware for pervasive environments in home, health care,

and energy management domains.


April 2012 94 CERTH

2.5 Enterprise Business Modelling Enterprise modelling is the set of activities or processes used to develop the

various parts of an enterprise model to address some desired modelling finality

[144]. It can also be defined as the art of “externalizing” enterprise knowledge,

i.e. representing the enterprise in terms of its organization and operations (e.g.

processes, behaviour, activities, information, object and material flows, resources

and organization units and system infrastructure and architectures). The prime

goal of enterprise modelling is not only to be applied for better enterprise

integration but also to support the analysis of an enterprise, and to represent and

understand how the enterprise works, to design (or redesign) a part of the

enterprise, to analyze some aspects of the enterprise, to simulate the behaviour

of the enterprise in various environmental cases, to improve the decision making

mechanism of the enterprise, or to control, coordinate and monitor some parts of

the enterprise.

In Adapt4EE the ultimate goal is to define a rich set of enterprise data models

that are abstract enough to model the business processes of the two pilot

domains (Hospitals and Multipurpose Office/Commercial Spaces), fully capturing

the energy related activities, namely ‘skeleton activities’ that could directly affect

enterprise energy performance (components/assets operations, HVAC, lighting,

etc). Thus, within the project lifetime focus will be given mainly on the business

processes and their ‘skeleton activities’ rather than the enterprise as a whole

when selecting the underlying modelling approach.

2.5.1 Enterprise Modelling Approaches As mentioned above modelling results in a desired finality or model, which is a

reproduction of the part of reality which contains the essential aspects to be

investigated from a specific point of view. For instance, an architect creates a

design of a building by creating a plan (model) which represents an actual

building. There may be several different building plans that focus on specific

viewpoints: a plan which contains all the plug sockets and wires in a building is

used by an electrician for the electric installation, or a plan that shows all the

plumbing and water taps for the plumber. So in order to support different

viewpoints a set of different modelling languages are available.

This section of the chapter classifies and assesses modelling languages and

approaches according to several dimensions as depicted in Figure 26.


April 2012 95 CERTH

Perspectives

Aspects

Language families

Bu

sin

ess

ITFormalisation

Perspectives

Aspects

Language families

Bu

sin

ess

ITFormalisation

Figure 26: The dimensions of the classification framework

Perspectives - this dimension clarifies the role of the user and the

application fields of the modelling language.

Aspects - this dimension relates to what should be modelled

and thus deals with the application fields and

resulting modelling concepts.

Formalisation - this dimension allows analyzing the level of

formalisation and the graphical notation, i.e. syntax,

the semantics and expressiveness of the languages.

Language families - this dimension group modelling languages together

which are based on a common philosophy.

2.5.1.1 Dimensions of the modelling language - Description

Framework for enterprise modelling

Different stakeholders or roles will have different viewpoints on each of these

aspects. The owner of any company is only interested in the elements that

correspond to the business strategy or the business model while a member of the

IT team only requires information on the implementation details. This results in a

two-dimensional model structure.

There are various frameworks for describing the elements of an enterprise

architecture such as the Zachman Framework [146], the ARIS Architecture for

Information Systems [145], or the BPMS Framework [147].

In the following we look at some of the frameworks in more details. Figure 27

shows the two dimensions of the Zachman framework. The columns correspond

to the different aspects and the rows correspond to the perspectives. The cells of


April 2012 96 CERTH

the structure show examples of models and the main concepts to be represented

in these models.

Figure 27: Perspectives and aspects of the Zachman framework ([146])

These two dimensions can also be found in the business process management

frameworks. The ARIS Architecture for Integrated Information Systems [145]

distinguishes between views and descriptive levels, which correspond to aspects

and perspectives, respectively. In Figure 28 the aspects (views) are represented

as boxes and the perspectives (descriptive levels) as levels in these boxes.

Figure 28: Perspectives and Aspects of the BPM Frameworks ARIS ([145])

For the BPMS approach (Figure 29) the perspectives correspond to the sub

processes of business process management while the aspects depend on the


April 2012 97 CERTH

application scenario. For the business process management of ADONIS® the

aspects correspond to the core elements.

Figure 29: Aspects and Perspectives of BPMS

2.5.1.2 Relations between the dimensions

The different dimensions described above are orthogonal. In order to explain the

relationship between the perspectives and aspects, Figure 30 illustrates the

relevant topics of different available models and the potential combinations will

be considered as a specification framework. It highlights that a combination does

not exist for each model. For instance, products are not modelled at an

information system level so far. Also, no model exists that relates the IT aspect at

the strategic perspective layer.

Figure 30: Models associated to perspectives and aspects


April 2012 98 CERTH

There are many modelling approaches, languages and standards available to

support the underlying frameworks that we already mentioned. Table 6 presents

an overview of the various enterprise modelling languages and standards

available and the colour cells represent how they align to the aspects and

perspectives of Figure 30. Although not all of the standards represented are

focused on graphical modelling they are still important for Adapt4EE in relation to

the interoperability of the various parts of the simulation framework.


April 2012 99 CERTH

Table 6: Overview of the modelling approaches and standards in Enterprise Modelling

Aspects Perspectives

Modelling Approaches

Strategy Business Systems Technology Data

/Knowledge Process

People /Organization

Applications Products Motivation

ARIS - Architecture for Integrated Information Systems

EPC - Event Driven Process Chains

ASTD - Application System Type

Diagram

Function Tree

Organigram

Value Added Chain Diagram

eERM (extended Entity Relationship

Diatram

Knowledge Map

Technical Term Model

BPMS

Product Model

Company Map


April 2012 100 CERTH

Business Process Model

IT System Model

Document Model

Working Environment Model

Standards

Object Management Group (OMG)

BPMN - Business Process Modelling

Notation

BPDM - Business Process Definition

Metamodel

SBVR - Semantics of Business

Vocabulary and Business Rules

BMM - Business Motivation Model

PRR - Production Rule Representation



The World Wide Web Consortium

(W3C) Specifications

XML - Extensible Markup Language

RDF - Resource Description Framework

OWL - Web Ontology Language

SWRL - Semantic Web Rule Language

WSDL - Web Services Description

Language

Workflow Management

Coalition (WfMC)

XPDL - XML Process Definition Language

Organization for the Advancement of Structured Information Standards (OASIS)

BPEL - Business Process Execution

Language



2.5.2 Enterprise Modelling Applications and Scenarios Enterprise Modelling is the art of externalising and sharing enterprise knowledge

with the use of representative models which present these issues in a high level

format that is easy to understand from non-technical staff. The meta modelling

concept is used to widely apply enterprise modelling in many different application

scenarios. Figure 31 illustrates sample components of an enterprise model.

Figure 31: Sample Enterprise Model Components

The following application scenarios for enterprise modelling presented can be

categorized into Business, Legal and IT domains.

2.5.2.1 Business – Process Documentation and Structuring

Enterprise modelling and more specifically in this case business process modelling

is used to structure the processes of a company using a defined method. All the

processes should be documented and structured in a way that is consistent and

therefore understandable by all. Anything that is performed more than once or by

multiple people or systems should be documented so that it can be monitored

managed and improved. If a process is not documented or structured it is like

running a computer system with no source code. It is fine once everything is

running smoothly but how will you fix it when it breaks down. Or in the case for

business it is about how to ensure that you retain the knowledge of the employee

if he or she were to leave the company.

2.5.2.2 Business – Process Optimization and Cost Calculation

Once processes and other parts of the enterprise have being modelled the next

step is to look at process optimization. The goal is to adjust a process or related

area of the organisation so as to optimize some specified set of parameters



without violating some constraint. The most common goals are minimizing cost,

maximizing throughput, and/or efficiency. The enterprise models are the

foundation of any process optimization efforts. Costs can also be evaluated

through the use of enterprise modelling and a common goal is to ensure that you

do not affect your value-adding activities through analysis. So instead by trying to

minimize execution times through structured simulation runs on the existing

models potential improvements will be discovered. Many improvements to an

organisation can be achieved without the need for complex simulation purely

based on the structured models sometimes directly at the highest level of

abstraction by discovering redundant systems, roles or activities.

2.5.2.3 Business – Performance Management

To design and model business strategies, the business organisation needs to be

broken down into its core operational management components and the resulting

components need to be measured. These operational components tend to be

completely independent when they are combined, they can increase the

performance and the company’s sustainability. Enterprise modelling allows you to

achieve a view of all the components and to apply a performance measurement

framework such as the Balanced Scorecard to them. A precise knowledge of the

relations and interdependencies of the company is required to perform conscious

controlling and to be able to measure the results. The effect of performance

management based on enterprise models depends significantly on the

establishment of responsible roles, as well as a clear allocation of rights and

duties. Only then can the management efforts be clearly converted into results

for the organisation. Process performance management also has to be integrated

into the enterprise controlling system to qualify and quantify the efforts for

process optimisation and re-engineering initiatives.

2.5.2.4 Legal – Governance, Risk and Compliance

Internal as well as external factors, such as EU guidelines or regulations can

require the application of a clear and effective internal controlling system (ICS)

from companies in all sectors. Internal controls that exist in various industries can

have very different levels of maturity. Either no documentation exists or the

controls are insufficiently documented or there are controls that are documented

to such as low level that they do not target the risk profile of the company which

can lead to a case of “over control”. Process Governance is more than just a

buzzword: transparent and clearly defined processes are a part of the

organisational culture which should be demanded when establishing a governance

framework. An enterprise modelling framework can support the creation of such a



governance framework by modelling the processes, organisation, systems,

documents, risks and controls and establishing interdependencies between the

various components. This will simplify the auditing process by ensuring that the

organisation is compliant with whatever regulations related to risk etc. that it

must adhere to.

2.5.2.5 IT – Process or Model Based Application Development

Enterprise Architecture and IT Service Management are two of the main areas in

the IT domain where Enterprise Modelling plays a major role. All elements of the

IT architecture of an organisation and their interdependencies can be represented

in enterprise modelling such as the hardware, software, data, applications,

services, risks, processes and roles etc. Efficient and effective Enterprise

Architecture Management assists the organization to align their IT to their

business by bridging the gap between the business strategies and the IT

architectures. EA-driven IT consolidation tries to discover potential improvements

to the IT landscape to utilise fewer resources whilst operating more efficiently and

providing better IT services to the organisation.

Figure 32 illustrates additional applications and scenarios where an enterprise

model can support an organisation.

Figure 32: Application Scenarios of a Collaborative Enterprise Modelling Environment



2.6 Visual Analytics Technologies

2.6.1 Introduction and terminology

Visual analytics is the science of analytical reasoning supported by interactive

visual interfaces [149]. It is a novel research field that aims to provide

visualization-based methods for human decision processes analyzing large and

complex information in a variety of fields. Scattered work in the beginning and

more systematic as the years passed, contributed to the formation of the Visual

Analytics research area as a step forward from the plain Information

Visualization. This new discipline aims to bring together the best of both worlds

(Figure 33): the vast processing power of the computers and the unique human

traits of cognition, holistic thinking and insight.

Figure 33: Visual Analytics: The best of both Worlds (from [150])

Being more specific, during the recent years, following the massive evolution that

has been realized in the areas of data mining and computer graphics, intelligent

information visualization, namely visual analytics, have emerged as one of the

most prominent as well as effective solutions to the problem of managing,

processing and analyzing high volumes of data in a variety of domains such as

engineering analytics, business, environmental monitoring, disaster and

emergency management, and so on.

In particular, founded on a solid multi-disciplinary basis, visual analytics

consolidate the merits of system analytics and visualization, in order to maximize

the efficiency of machine automation mechanism through the incorporation of

human inherent intuition and background knowledge into the decision making

processes. Visual analytics differs to classic information visualisation in that it

attempt to take into account properties of human cognition, perception and sense

making. Essentially, visual analytics aims to develop appropriate visualisation and

interaction mechanisms that match the mental processes of humans.



In this context, visual analytics are commonly accepted to hold the unique

advantage of being able to make virtue out of the information overhead and

disparity that characterizes the majority of the modern systems and thus to turn

this apparent drawback into an opportunity. Hence, within this framework of

semi-automated human-machine interaction and collaboration, visual analytics

overcome the limitations of the usual threshold-based decision support systems,

providing the analyst with a profound insight to the status and dynamics of the

system under investigation [149]-[151].

2.6.2 Definition of Visual Analytics “Visual analytics is the science of analytical reasoning supported by interactive

visual interfaces”. This definition is given in the Research and Development

Agenda for Visual Analytics, entitled “Illuminating the Path” published in 2005

[149], a book that has made a great impact on the research community since its

release. The goal of this new discipline is to turn the information overload into an

opportunity, aiming to answer a growing range of questions in science, business,

and analysis whose size, complexity and need for closely coupled human and

machine analysis may make them otherwise intractable.

Information visualization can be seen as an iterative process that involves

information gathering, data preprocessing, knowledge representation, interaction

and eventually decision making, with the ultimate goal to generate insight in the

user’s mind and consecutively a solution to the problem they are investigating

[150], [152]. A visual representation of this process is illustrated in the following

Figure.

Figure 34: The Visual Analytics iterative process towards improving decision making of

involved users



In Figure 34, the ovals depict the different phases of the visual analytics process,

whereas the arrows denote the transitions among them. As seen, the first step is

the preprocessing of the data composed of steps such as normalization, grouping,

data cleaning, etc. This is often an important phase of the visual analytic process

since in many cases the data can be heterogeneous (combination of information

in the 2D and 3D space, media, and other kind of data). Then, there are two

directions that lead to the desirable knowledge and insight. The first is to visually

map the processed data, then by means of user interaction such as zooming or

selecting specific parts of the information, to extract meaningful conclusions. The

second is to generate a hypothesis regarding the input data, build the

corresponding model and then by information visualization and interaction to

validate or not the initial hypothesis. The whole process for both can be iterative

whereas their combination is feasible and could be used in improving the decision

making process.

Towards the design and effective visual analytics technologies the following

dimensions shall be taken into account:

� Context of Use / Domain, in which the purpose(s) of the analytical

reasoning shall be clearly specified and analyzed.

� Data Availability / Features, in which the appropriate data

representations and transformations shall be performed, namely the pre-

processing procedure illustrated in Figure 34.

� Data Visualization / Presentation, in which the selection of the visual

representations and interaction technologies take place. Here, also resides

the production, presentation and export of the visual analytical findings to

the corresponding stakeholders (end-users).

From the above dimensions, data representations and transformations consist of

the basis on which visual analytics is built, whereas the use of visual

representations mechanisms including interactions can assist the involved users

to gain deep insight into complex data, as this is the case of visual analytics tools.

Moreover, visual analytics through intelligent data exploration provide end-users

effective visual representation schemas towards efficient scanning of diverse

information and allowing users to immediately recognize trends, gaps, minima

and outliers, minima and maxima, clusters and other useful structural information

of the data.

As of today, visual analytics applicability is being continuously extended to cover

a great variety of numerous scientific fields, where the requirement for timely

processing huge bulk of data against several competing performance factors



poses an unbearable burden to the traditional analysis methods and tools. In

more detail, visual analytics’ capabilities have been so far extensively exploited in

astrophysics [153], finance [154]-[155], disaster management [156], software

development [157]-[158], biology/health [159]-[160], transportation [161], in

telecommunications security [162]-[163] as well as in the AEC industry [45],

[173]-[178] .

2.6.3 Data visualization in the AEC industry Visualization has been always used as a visual aid to interpret analytical data.

Historically, a rich literature has been developed related to 2D, 3D, 4D and even

nD visualization of construction products. Within AEC industry, there is a need to

define methods of organizing, studying and communicating building-related data

in a manner that makes feasible a more holistic understanding of building

performance in relation to the spatial and contextual information of the facility at

all stages of its lifecycle.

Several concept design tools such as Sketchup, Rhinoceros and Bonzai3D have

shown that 3D modelling in the conceptual stages of a facility can be used to

support performance analysis and simulation applications. For instance, Google

Sketchup tool supports the integration of plug-ins towards extending its initial

purpose, the conceptual design of BIM products. Several plugins have been

developed that support both simulation and data visualization in terms of building

energy performance analysis. Indicatively, the IES VE plugin enables to run quick

“indicative” energy performance for heating and cooling. Also Open Studio, which

provides a set of smart functionalities for BIM-related information available in its

model. For instance, Open Studio enables BIM data to be mapped in zonal

interfaces and provides Architects & Engineers (A&Es) the possibility to perform

instantly energy performance analysis using the EnergyPlus engine (through IDF

input representation).

Furthermore, the latest version of commercial products such as Revit (2011),

added new capabilities that further support the analysis and visualization of data

related to energy analysis. For example, Revit’s sketch design can be interfaced

with Ecotect Analysis and Green Building Studio. Moreover, other sketch tool such

as ArchiCAD provides interfaces to EcoDesigner, energy analysis software that

can be used at the early stages of a building design. The deployment of such tools

in the AEC industry provided key stakeholders enriched graphical user interfaces

to display simulation results dynamically on top of the BIM models.

Hailemariam et al. ([174]) denoted the limited research in deploying visual

analytic technologies in building performance simulation tools and proposed a



unified representation for building simulation analysis, where various simulated

and measured data are visualized in a high-resolution 3D environment. By

representing data visually and within the 3D BIM model, authors in [174]

envisaged to leverage the affordances provided by the BIM model towards

allowing end-users to better understand visualizations (Figure 35) in relation to

space.

Figure 35: Combination of colouring, texts and segmentation (peg glyphs) to visualize office occupancy in a 3D building model [174].

The aforementioned visualization system needs further enhancement in terms of

allowing analysis of more complex data in the spatio-temporal domain. For

instance, the temporal aspect is not fully taken into account in the system, thus

spatio-temporal analysis on energy performance and how human occupancy or

interaction with Building Spaces and Zones is not feasible.

An early prototype that creates spatio-temporal representations of (observed

and/or assumed) geometry, occupancy, environmental and performance data,

and enables users to visualize and explore how this information changes through

time in a 3D environment has been presented by authors in [45]. The proposed

system uses as input real or simulated temporal data related to building

occupants, energy consumption or environmental information along with a 3D

building model. The overall conceptual framework is illustrated in the following

Figure.



LegendLegend

Preview of Information

Source of Information

LegendLegend



LegendLegend



LegendLegend



Figure 36: Narrative of the method proposed by Akbas et al. ([45]) for temporal

visualization of building performance highly related to its occupancy.

It is built on the visualization engine of GSim ([175]), which means it provides

similar interactions with 4D CAD. For visualization purposes, various techniques

are used, such as colour-coding, text, time-series graphs and vectors. The

visualization framework offers to the end-users 3D and temporal navigation as

well as comparative analysis. Moreover, the user interface enables users to

analyze occupancy either in 3D model or separate in 2.5D layers, taking into

account both spatial and temporal data in GIS. Results provide annual energy

usage, month-by-month heating and cooling, and breakouts by energy load

contributions. Some of these results are mapped to the external zone boundaries

and color coded, for enhanced visualization.

A recent study on visualizing patterns in building performance data has been

performed by authors in [177]. Raftery et al. proposed a novel visualization

technique based on a carpet-contour plot of the baseline energy-related data. The

technique is useful for transforming and analysing building performance data. A

case study of this visualization technique is illustrated in the following Figure.



Figure 37: Carpet-contour [177] plot illustrating mean value of HVAC electricity consumption against other information such as solar irradiance and dry-bulb temperature.

The visualization technique has been proposed by authors in [177].

Visual analytics on 3D building model views can be very effective and attractive

for end-users towards analyzing and visualizing information related to the

building performance in the spatio-temporal domain. In this direction,

customizable functions for generating BIM models views that are colored

according to a variety of performance status data (e.g. occupancy in building

spaces, thermal analysis, lighting simulation, energy consumption on the building

envelope and in its zones, etc.) are high beneficial. In this direction, there are

also early design frameworks that emphasize in application specific functional

workflows within a BIM model. For instance, Trelligence [176] software provides

space planning layouts with data visualization and feedback on space

programming against design targets. In all cases, data dimensionality is an

important aspect of visualization and by integrating both spatial and non-spatial

building performance data into a 3D environment, new possibilities are introduced

regarding the understanding and analysis of a building through its entire life

cycle.



Concluding, as the green design movement matures, it becomes increasingly

important to seek evidence of the effectiveness of green design from the

simulation of the physical performance of a building or space when proposing

green design options. Consequently, the use of BIM based simulations and

analysis is becoming important in green design practice. The incorporation of

intelligent information visualization methods in complex data views such as the

one BIM models introduce will help designers & engineers to understand how

their designs will perform, especially when the effects of particular choices go

against the architects’ intuition [172].



3. Market Analysis 3.1 Introduction According to McGraw-Hill Construction research findings [5], users in the AEC

industry gain bankable benefits by using various Building Information Modelling

and Building Performance Simulation tools in their daily working life, as those

enhance their productivity, improve their ability to integrate teams and

collaborations among diverse AEC stakeholders (e.g. engineers with architects,

designers with building tenants/property owners, etc.) and eventually give them

the necessary advantages on the competition. The following sections of the

chapter provide a detailed market analysis on the existing tools and platforms in

the AEC industry related to BIM and BPS software, focusing but not limited to the

early design phases of a construction product. Through the analysis of the

available software, a gap analysis is performed along with identification of the

market success factors provided by key users of such software.

Moreover, as Adapt4EE addresses also the enterprise aspects during the early

design phases of a building, a separate section is provided that presents some

major products and commercial tools used in Enterprise Modelling and could be

set as reference groundwork for the respective tools that will be developed in

Adapt4EE. As for BIM and BPS tools, the chapter concludes on the future market

needs, trends and opportunities on enterprise modelling and several aspects

related to Adapt4EE concept are outlined.

3.2 Building Information Modelling (BIM) and Building

Performance Simulation (BPS)

3.2.1 Major BIM products & tools BIM tools represent buildings as 2D or 3D objects. Depending on the product,

these objects may be abstract and conceptual, or construction detailed. Modern

BIM products can define objects parametrically, i.e. as parameters and relations

to other objects. In this section, the results of the market survey performed on

major BIM products is presented in Table 7 outlining the main features of each

product along with a small description for its usage as a BIM tool.

Table 7: List of major BIM tools used in the market during the whole life cycle of a

building (from early design to the operational phases)

Product Name

- Vendor Main Features

Supported

Formats Description

Allplan

Architecture -

Object-oriented

3D design, Cost

IFC, DWG,

DXF, PDF

Provides methods and tools to many

disciplines along the lifecycle of a



Product Name


Supported

Formats Description

Nemetscheck planning and

detail drawings,

CINEMA 3D

construction product, like 3D modelling,

quantity take-off, cost estimations,

tender management and facility

management tools. It’s a web based

suite, supporting collaboration between

larger and geographically distributed

project teams.

Archicad -

Graphisoft

Object-oriented

approach,

architectural

modeling, cost

estimation,

energy

analysis,

Server-based

modeling,

collaboration,

translators

IFC, DXF

The software uses material libraries and

parametric objects to easily create 2D

and 3D models. It includes a

collaborative platform environment to

facilitate shared BIM projects.

Modelling and analysis tools include IFC

reference model, model mapping,

element classification, version tracking

and change management.

AutoCAD Civil

3D - Autodesk

BIM solution for

civil

engineering.

Integrated

geospatial

analysis,

intelligent pipe

layouts,

hydrological

analysis and

production

drafting.

DWG, DXF

and DWF

Supports design, analysis, construction,

visualization and documentation

activities.

Modules are provided for dynamic

quantity take-off, geospatial, pipe

layouts, earthworks and storm water

analysis and calculations. The platform

provides features and functions to

support activities such as criteria-based

geometric design, parcel layouts,

corridor modelling, pipe layouts and

surveying, making it possible to be used

in infrastructure projects such as road

and highway design, bridge design, land

development and environmental

projects.

The product provides connectivity

between design and documentation



Product Name


Supported

Formats Description

provides rich features for annotation,

plan production documents, reporting,

and coordination.

Bentley Suite -

Bentley

Design and

modeling for

infrastructure,

architecture,

structures

design and

MEP.

DWG,

gbXML, IFC,

Adobe 3D

PDF, DGN,

STEP,

SketchUp,

Rhino and

IGES

Bentley software provides several tools

that support activities along the lifecycle

of construction.

The software supports collaboration

capabilities and integrates with

specialized tools such as RAM and

STAAD for structural analysis.

Moreover, it includes tools for 3D

parametric modelling, surface and solid

modelling, schematics, harness and

cable modelling, building electrical

systems, mechanical systems, gas and

water piping and so on.

Bluethink –

Selvaag

Bluethink

Rule-based 3D

house designer.

Sketch, cost

estimation,

design check.

Major CAD

formats and

IFC

The software supports major CAD

formats and IFC standard.

It captures design intent and layout

from user sketches.

Rules can be easily applied towards

assisting in creating BIM models.

CATIA –

Dassault

Systems

3D surface and

solid modelling

environment.

DWG, DXF,

IGES, STEP,

STL, CAD

formats

The software is extensively used in

aeronautical industry for product

modelling and product lifecycle

management.

It offers a wide range of modelling

tools, integrated analysis capabilities

and visualization support. Also supports

integration with many analysis tools.

CostOS BIM –

Monitech inc.

BIM-based cost

estimation.

Quantity

surveying and

risk

management.

Major CAD

formats and

IFC

The software creates bill of quantities

directly from 3D model and populates to

an integrated database.

The cost criteria are based on the

resources used; such as material,

equipment, labour, and consumables.



Product Name


Supported

Formats Description

Supports cost viewer, navigating and

searching features and cost comparison

in the environment.

Other modules support estimation for

quantity surveying and database

management.

DDS – Data

Design System

Electrical,

HVAC,

collaboration.

Supports

project

management,

MAP systems

and

manufacturing

Major CAD

formats and

IFC

DDS suite provides tools for MEP

component design, architecture and

construction especially for timber frame

design buildings.

The architecture tool provides

parametric modelling to design the

buildings. The construction tool provides

the modules for joist design, wall panel,

floor and roof.

This design data can be interfaced to

production and fabrication. The MEP

module supports the design and

documentation of the HVAC, electrical

and plumbing systems.

Finally, IFC interface is available to

communicate data with other external

programs.

EcoDesigner –

GRAPHISOFT

Energy analysis

and cost

estimation

Graphisoft

native

format

Serves as a tool for the early design

phase and to derive design

configuration based on energy use.

Some of the basic features include:

model review, visualization in 2D and

3D, use of weather data for energy

computation. EcoDesigner captures the

materials’ data used in the design and

uses these values to perform the

thermal analysis.

Ecotect -

Autodesk

Sustainable

building

software.

Whole building

energy

gbXML, CAD

formats,

Sketchup,

Graphisoft

It is used in the process of deriving

sustainable design configurations.

Supports simulation, energy analysis



Product Name


Supported

Formats Description

analysis,

thermal

performance,

water usage,

solar

calculations,

shadows and

reflections

format. and facilitates integration of

environment data for energy

computation and building performance.

The 3D representation of the building is

separated into spaces/zones. Also

provides thermal analysis of structures

and shadow analysis based on the sun

path and the BIM model information.

Revit Structure

– Autodesk inc.

Structural

design and

drafting. Clash

detection, form

functions,

detailing,

quantity take-

off, scheduling

and vibration

analysis.

DWG, IFC,

FBX

Structural components and assemblies

conception to construction

documentation. Supports structural

analysis, creates shop drawings to

facilitate fabrication from the model.

It consists of modelling tools for

structure elements such as beams,

columns, braces, joints, etc. Special

modules are available for concrete

reinforced modelling and rebar design.

SketchUp Pro -

Google

3D modelling,

3D

documentation

and

presentation.

Attribute data

population and

watermarks.

Google

Earth,

popular 2D

image

formats,

COLLADA,

VRML, DWG,

DXF, FBX

SketchUP Pro is a 3D modeling software

for design and visualization. It allows

generation of quick 3D models from

sketches, overlays and aerial imagery.

The models can be rendered to provide

high resolution images to be shared and

communicated with other applications.

It provides layout, 2D documentation

and presentation tools to create and

share design and has the ability to add

attributes and create dynamic

components.

SketchUP Pro allows interactive

presentations using sheet layouts, style

browsers and material library making it

suitable for conceptual design review.

Simlab Soft –

Simulation Lab

Software

Imports major

CAD/BIM

models, scaling

and scene

Popular

CAD

formats,

Collada,

Simlab Soft software provides creation

of 3D PDF versions of construction

documents.

It provides features such as rendering,



Product Name


Supported

Formats Description

preparation 3D

PDF. Integrates

with major CAD

and BIM

formats

FBX, STL,

Google

SketchUp,

Rhino3D

and many

Autodesk

products

such as

3ds Max

and Maya.

animations to enhance the models. The

models can be used to create 3D scenes

with photo realistic rendering. It

provides a programming approach to

build interactive visualization

applications.

It organizes and generates a tree

structure of the model components.

Finally, it has the ability to publish the

scene in many graphic export formats.

Solid Builder –

Digital Canal

Corporation

3D building

blocks and

templates,

framing, roofs,

quantity take-

off

DWG, DXF Building design software tool. Design

automation and analysis for residential

buildings using the concept of building

blocks. User defined rules for building

blocks can be used.

Also contains tools for structural

engineering, design engineering, cost

and quantity estimation, scheduling and

resource management and more.

STEEL – 4M Design and

analysis of steel

structures.

Dynamic

spectrum

analysis and

member

connections

DWG, DXF Is an interactive software environment

for creation and analysis of steel

structures, including its tructural

elements and components.

Full detailed geometry based on

calculations can be created using

additional modules such as “PreSTEEL”

or “SteelCONNECT”.

STRAD – 4M Design and

analysis of

concrete

structures.

Dynamic

spectrum

analysis

DWG, DXF STRAD is a software environment for

creation and analysis of steel and

concrete structures. STRAD performs

both space frame analysis and design

modeling of member components.

The individual member design modelling

is accomplished by either AutoCAD or

IntelliCAD CAD environments. The

analysis is carried out using the Finite

Element Method. The STRAD suite of



Product Name


Supported

Formats Description

tools provides capabilities ranging from

modelling, analysis and simulation to

production drawings.

Tokoman –

Digital Alchemy

BIM server,

quantity take-

off, estimation

and scheduling.

Integration with

other BIM

packages

IFC, CIS/2,

ArchiCAD,

Tekla, Revit

Model-based quantity management

solution.

The software has BIM server

capabilities; it can be used for cost

estimation, quantity take-off, and

construction scheduling.

The tool provides a link with BIM and

structural design software, as it

provides a module called ―ilinks which

integrates Microsoft applications such as

Visio and other standard office tools.

VectorWorks -

Nemetschek

general

purpose, highly

customizable,

2D, 3D and

data rich

software

suitable for

CAD and BIM

applications.

DWG, DXF,

3DS, IFC,

VectorScript,

IGES, SAT,

STL, KML.

VectorWorks-Nemetschek is an

architectural software offering high

resolution rendering, 2D dimensional

constraint modelling and 3D modelling.

Building data can be entered in a

preformatted space planning worksheet

with areas and adjacency information.

The colour, pattern, score and

connecting lines of these squares give a

visual interactive environment to assist

space planning. These space boundaries

can be converted to construction

elements such as walls which can

quicken the design process.

VICO Suite –

VICO Software

Integrating

construction

activities,

quantity take-

off, cost

estimation,

virtual

construction

IFC, Revit,

Tekla,

ArchiCAD

VICO Suite is a set of software modules

for the development of virtual

construction models. These models can

be used for activities such as scheduling

construction processes, cost estimation,

quantity take-off, producing shop

drawings, etc.

VICO tools support construction



Product Name


Supported

Formats Description

management for large projects

specifically for 5D BIM applications.

Features provided in the software

include interference checking,

publishing the models, review,

specifying constructability issues and

redline and mark-up capabilities.

It provides visual analysis of schedules’

feasibility, what-if analysis, forecast and

optimized schedule planning. Finally,

the document set manager automates

the process of checking revisions of 2D

drawing sets and the changes are easily

recorded and documented.

Table 7 lists only a limited number of available BIM software used in the AEC

industry. From the conducted market survey on BIM-related software, it is

evident that the use of product models (e.g. BIM) and supporting model servers

has been under significant research and development in the last two decades.

While a large number of BIM software do exist, including some early model

servers, significant challenges still do remain as summarised below:

� Despite significant developments in technologies, interoperability and data

interchange among different software vendors remains a major challenge.

With changing discipline specific data models, it is difficult to continue to

exchange data in a meaningful way. When there is the possibility to

exchange data, this is typically on the basis of the minimal common

denominator of shared data. In most cases, the software read-in (import)

data and then map this on to the internal data models of the

application/discipline and then later export this out. When exporting, data

from an application’s own data model to a common/shared data model,

there is always the risk that application/discipline specific additions are

dropped / omitted during the export.

� Even when BIM is in use, data sharing and collaboration among key

stakeholders is still fundamentally file-based. This leads to redundancy,

lack of notification in case of changes to a model, and delayed

communication of information. Moreover, it is also difficult to keep track of

the “latest” and “correct” version of a building’s information model. There



have been developments around BIM model servers, but much more

research is needed to fully make use of “distributed” model servers

ensuring data coherency, real-time information access, etc.

� There is still a lack of interfaces between different disciplines engaged

during a building’s design and commissioning phases. Most disciplines

continue to have their own data models and only some of these have some

form of interface to import/export data from a common shared BIM. BIM

model servers and especially distributed model servers still require further

research and development.

Section 3.2.5 “Conclusions, Future Market Needs, Trends and Opportunities”

provides in more details future trends in the AEC industry as well as opportunities

for software vendors dealing with integrated BIM models.

3.2.2 Major BPS products & tools Building performance simulation (BPS) tools [125] predict the energy

performance of a given building and support the understanding of how the

construction product will “operate” according to certain criteria and characteristics

(e.g. space layout, environmental loads, occupancy schedules, HVAC systems and

components, and thermal simulation).

Most of the available tools enable comparison of different design alternatives and

can be considered as a fundamental part of the design process [132]-[133]. The

importance of their applicability in the early design phases of a construction

product can be further justified by the fact that the design decisions made in this

level of development may have large impact [134]-[135] on all design decisions

on later stages of the building development.

A general overview of the data flow (provisional architecture) of a building

simulation engine (as part of an integrated BPS tool) is illustrated in Figure 38.



Figure 38: General data flow of Building Performance Simulation engines

The main input to a building energy performance simulation engine depends on

the level of development of the construction product. It consists of the building

descriptive data (geometry, material), internal and external loads, HVAC systems

and components, schedules and operating-related strategies.

For instance, internal and external loads provide the information needed for

predicting energy consumption in the spatio-temporal domain. The external loads

relate mostly to environmental conditions (e.g. statistical models for weather data

for a given location or region), whereas internal loads refer mostly to the loads of

occupants, lights and assets of the building under design, which depend gradually

on the actual building space layout design and the space utilization in real-life by

its occupants. It should be noticed that the internal loads regarding occupancy

schedules remains a challenging feature in the available BPS tools, as existing

tools, mainly utilize statistical models for occupancy during building energy

simulation analysis and it is obvious that the assumptions made about the

quantity of the occupancy loads (not fully taking into account the actual building



space planning and human behaviour and presence) in the spatio-temporal

domain may lead to less realistic simulation results.

One additional critical input that is rather underestimated by existing BPS tools

([124], [125], [127], [129]) is the simulation specific parameters that are

essential for the execution of the underlying models of the building simulation

engine. These parameters play an important role for the behaviour of the core

simulation engine (initial configuration and parameterization) and can be used by

end-users (architects, designers, engineers, etc) to further analyze the effects of

their design alternatives.

Last but not the least, every BPS tool provides various visualization mechanisms

(comparison among design alternatives, user-friendly interfaces for results

analysis, etc) which are further analyzed in separate section (Section 2.6-Visual

Analytics Technologies) of this report.

During the recent years, extensive surveys have been published for BPS tools

[126]-[129] that include detailed comparison of most dominant building energy

simulation products towards providing an up-to-date evaluation and assessment

of their features and capabilities. Survey findings indicated that each tool may

have limitations [124] when performing various building energy-related

simulations, whereas several studies express that current tools are inadequate,

user hostile and incomplete to be used by Designers & Engineers (D&Es),

especially during the early phases of building design [129].

The most important categories used for comparison [126] are illustrated in Figure

39, whereas the most important criteria and characteristics for the classification

of the BPS tools [129] are illustrated in Figure 40.

Figure 39: Major categories for evaluating the capabilities of building energy simulation

tools



Figure 40: The most important selection criteria for BPS tools by AEC community

Both figures provide findings stemming from existing literature and recent

surveys on building performance simulation tools, in which the end-users

community in the AEC domain (designers, engineers, architects, planners, etc.)

outlined the main criteria for the selection of the BPS tools. A deeper analysis for

the current BPS tools gaps and future trends are provided in section 3.2.4 “Gap

Analysis, Market Success Factors” of this deliverable.

The following paragraphs provide an overview of several building energy

simulation programs outlining their main features, data input/output supported

and general comments on the functionalities provided. It should be noticed that

focus is given on those tools that may be used mainly by D&Es in the early

phases of a design as well as from the AEC partners that participate to the

project. Readers may step through recent surveys [125], [127], [129], [137] for

a detailed comparison of major BPS tools based on the aforementioned criteria

and categories.



3.2.2.1 DesignBuilder

3.2.2.1.1. Software Overview

DesignBuilder [138] is a major commercial BPS tool that enables D&Es for

checking building energy, carbon emissions, lighting and comfort performance.

The software allows D&Es to rapidly compare the function and performance of

building designs and provides a friendly graphical interface to many of the

features of the EnergyPlus simulation engine [140]. It provides one of the most

comprehensive user interfaces to the aforementioned EnergyPlus dynamic

thermal simulation engine. The software support D&Es to all stages of a building

design life-cycle (from early schematic designs to detailed design stages).

3.2.2.1.2. Capabilities

The capabilities of the software are detailed in the following paragraphs:

� Simulation & Design

o It can calculate heating and cooling loads using available standards

(e.g. ASHRAE implemented in EnergyPlus) taking into account

weather data based on the level of development of the building

under design.

o It can perform and execute simulations of the input building model

for a comprehensive range of time intervals (annually, monthly,

daily, hourly, etc.). Simulation data can be on energy consumption

broken down by fuel and end-use, internal air, mean radiant and

operative temperatures and humidity (based on site weather data),

comfort output based on ASHRAE 55 criteria, heat transmission

through building envelope, internal loads due to HVAC systems,

CO2 generation as well as many other simulation data reported in

the official web page of the tool.

o Environmental performance data is displayed without needing to

run external modules and import data and any simulations required

to generate the data are started automatically.

o EnergyPlus ‘Compact HVAC’ descriptions provide an easy way into

detailed analysis of commonly used heating and cooling systems.

o Natural ventilation can be modelled with the option for windows to

open based on a ventilation set point temperature.

o Daylighting - models lighting control systems and calculates

savings in electric lighting.



o Shading by louvres, overhangs and sidefins as well as internal and

mid pane blinds.

o Parametric analysis screens allow D&Es to investigate the effect of

variations in design parameters on a range of performance criteria.

o It can generate EnergyPlus IDF files and D&Es can work with these

outside DesignBuilder to access EnergyPlus system functionality not

provided by DesignBuilder in other BPS tools.

� Interface & Visualization

o The interface enables D&Es to control the level of detail in each

building model allowing the collaboration among key stakeholders

during the whole building design life-cycle.

o It can generate rendered images and movies of the building under

design including the effect of site shading.

o It enables D&Es to make global changes to the model of the

building, at building, block or even at zone level.

o It provides the functionality to explore the building model using

simple view and walk-through controls.

o It provides the functionality to export the building models to other

CAD applications such as SketchUP, AutoCAD, etc.

3.2.2.1.3. Data Input

Data input (location, climate, energy code, materials) can be done from scratch

or taking advantage of any of the default templates included in the software (see

Figure 41). Starting from scratch is quite complicated due to the large number of

options that must be completed so it is advisable to start with a template that is

similar to the case being analyzed.



Figure 41: DesignBuilder GUI – Recent files and templates for data input

Once D&Es open a template from the library or start a new file, the software

opens the Edit screen (Figure 41). In this main window end-users may find on the

left side a navigation panel to navigate among the different layers of the model

(site, building, block, zone, surface, opening), on the right side there is an

information and help panel (in the learning mode), at the bottom there are a

series of tabs that give access to different screens (Edit, Visualise, Heating

design, Cooling design, Simulation, CFD, Daylighting) and at the top (only Edit

screen) there are other tabs used to define the model (Layout, Activity,

Construction, Openings, Lighting, HVAC, CFD, Options).



Figure 42: DesignBuilder, overview of the interface tabs2 during model editing process. More information for the latest features and documentation can be found in official

software website [138]

Last but not the least, the software includes data from the latest ASHRAE

regarding weather statistics and location as well as the full list of the latest

EnergyPlus hourly weather files. Regarding interoperability, D&Es have the

capability to import 3D-CAD models using existing standards (e.g. gbXML).

3.2.2.1.4. Output

The tool in general provides interesting features for visualizing the simulation

results and enables D&Es to perform parametric simulations. The graphical

interface is well organized around several tabbed views; however the detailed

information given by the tool could sometimes impede the friendliness of use and

navigation. Some of the most important visualization aspects of the tool are

presented in the following paragraphs, whereas detailed information can be found

on the product manual.

DesignBuilder provides a realistic rendered view of the modelled building. The

shadows, North arrow and ground plane can be displayed, as illustrated in the

following Figure 43.

2 Source http://www.designbuilder.co.uk/helpv3/ (Getting started/ Edit screen). Please note that newer versions of Design Builder may have added more tabs.



Figure 43: BIM model visualization screen overview

Regarding heating and cooling designs, the EnergyPlus engine is being used with

the following main characteristics:

� Heating Design Simulation Main Features

o Constant (Steady-state) external temperature set to the winter

design external temperature.

o Wind speed and direction set to design values.

o Heated zones are heated constantly to achieve the heating

temperature set point using a simple convective heating system.

o Includes consideration of heat conduction and convection between

zones of different temperatures.

o Schedules are not used for Heating design calculations which are

based on a steady state analysis which does not account for timing

Similarly to Heating Design Simulation, it should be noticed that cooling

simulation continues until temperatures/heat flows in each zone have

converged. If convergence does not occur then simulation continues for the

maximum number of days as specified in the calculation options. Furthermore,

simulation calculates heating capacities required to maintain the temperature

set points in each zone and displays the total heat loss broken down to a)

glazing, b) walls, c) partitions, d) solid floors, e) roofs, f) external infiltration

and h) internal natural ventilation.



Figure 44: Heating design tab and simulation results output through DesignBuilder interface

� Cooling Design Simulation Features

o It supports the calculation of periodic steady-state external

temperatures based on maximum and minimum design summer

weather conditions.

o Includes solar gains through windows and scheduled natural

ventilation.

o Includes internal gains from occupants, lighting and other

equipment.

o Includes consideration of heat conduction and convection between

zones of different temperatures.

It should be noticed that simulation continues until temperatures/heat flows in

each zone have converged. If convergence does not occur then simulation

continues for the maximum number of days as specified in the calculation

options. The simulation estimates half-hourly temperatures and heat flows for

each zone and determines cooling capacities required to maintain any cooling

temperature set points in each zone.



Figure 45: Cooling design tab overview and simulation results in Design Builder regarding temperature, heat gains, humidity and ventilation.

In general for simulation purposes, the corresponding view of DesignBuilder

allows D&Es to control and parameterize the simulation process in terms of

simulation period, calculation options, solar options and several visualization data

in grid, graphs and tables depending on the level of development such as:

� Fabric and ventilation heat gains/losses, internal gains, temperatures and

outside dry-bulb air temperature.

� Site data - all site data.

� Comfort - inside air, the radiant and comfort temperatures, relative

humidity, ASHRAE 55 and various comfort indices. If you set Interval to 6-

Distribution you get temperature distribution curves.

� Internal gains - internal gains including equipment, lighting, occupancy,

solar and HVAC heating/cooling delivery.

� Fabric and ventilation - heat gains to the space from the surface element

(walls, floors, ceilings etc.) and ventilation. Negative values indicate heat

loss from the space.

� Fuel breakdown - fuel consumption broken down by system category

(building level only).

� Fuel totals - fuel consumption broken down by fuel (building level only)

� CO2 production - CO2 production by weight (building level only)

Furthermore the tool supports preview of simulation results (during completion or

pausing within a certain period) in terms of slices (using the corresponding

computational fluid dynamics tab), as illustrated in Figure 47.



Figure 46: Parametric simulation results within DesignBuilder, an overview for variations

in the different design variables.

Figure 47: Computational fluid dynamics within DesignBuilder.

Moreover, the tool supports day lighting simulations with an easy-to-use

interface, which supports the following:

� Map - a daylight distribution contour map on the working plane of the

current object is displayed.

� Grid - a table of summary daylight statistics for each zone is displayed,

including average, min and max daylight factors and uniformity factor

data.

� LEED v2 Credit EQ8.1 Report - generates documentation that can be

used towards obtaining LEED v2 IEQ 8.1 credits.



� LEED v3 Credit IEQ 8.1 Report - generates documentation that can be

used towards obtaining LEED v3 IEQ 8.1 credits.

� BREEAM Credit HEA1 Report, - generates documentation that can be

used towards obtaining BREEAM HW1 credits.

� Green Star Credit IEQ4 Report, generates documentation that can be

used towards obtaining Green Star IEQ4 credits.

Figure 48: Daylighting simulation results – an overview from DesingBuilder tool

3.2.2.1.5. General Comments

Strengths

DesignBuilder is a complete and reliable software, supports different levels of

data-input, ranging from general to detail. As such, the software can be used by

D&Es to the different phases of the design stages. It should be noticed that for

simple users the interface may be too complicated due to the large number of

options that it offers. Regarding simulation analysis for energy efficiency, the

EnergyPlus engine is used, which has been widely reviewed and validated with

corresponding evaluation protocols (e.g. ASHRAE/BESTEST protocols).

Intelligence (default templates, energy certificates, etc.) and interoperability

(importing BIM-based architectural models) are well supported by the tool.

Focusing on the early design phases, the tool capabilities could provide useful

information to support architects in the design of energy-efficient buildings.

Weaknesses

Although the calculation engine is EnergyPlus, it has not incorporated all the

features of this program. To perform a more detailed simulation D&Es should

export the file to EnergyPlus and make the required adjustments. Moreover, the



problem is that D&Es can not then re-import the modified file from EnergyPlus to

DesignBuilder for analysis. An other drawback refers to the output given by the

tool, which from one hand is very detailed and complex, on the other hand poses

several limitation regarding architect-friendliness [137].

In respect to Adapt4EE objectives, the tool lacks the support for space utilization

of the building under design, thus human factor and building dynamic behaviour

due to the organization that will be later “housed” in the facility and its occupants

is partially considered during the evaluation of the energy performance of the

building.

3.2.2.2 Autodesk ECOTECT Analysis


ECOTECT [139] is a complete conceptual design tool aiming to provide to the

D&Es the necessary functionalities for simulating the sustainability of conceptual

building designs. The latest version (2011) provides comprehensive analysis

capabilities such as the support for studying design alternatives, incorporates

various simulation functions for whole-building energy analysis, carbon emissions,

water usage and cost evaluation, solar radiation, thermal performance,

daylighting and finally visualization of the results. The tool targets mainly early

design phases of a construction product and is suitable for designers & architects.


The tool supports several simulation and analysis functions, which can be

summarized in the following bullets:

� Whole building energy performance analysis

o Shadows: displaying shadows over the geometry of the model.

o Overshadowing: calculating when objects are in shade and by how

much.

o Solar radiation: quantifying over time exposure to solar incidence.

o Shading design: techniques for the performance-based design of

shading devices.

o Natural and artificial lighting: the calculation of luminance levels

and daylight factors.

� Thermal performance analysis: internal temperatures, gains and heating /

cooling loads.

� Cost and environmental impact: fabric costs, embodied energy and

greenhouse gas emissions.



� Acoustic analysis: statistical reverberation and sound rays / particle

generation. The tool offers a wide range of acoustic analysis functions and

can establish a direct link between the geometry of a space and its

acoustic response.

� Cost and environmental impact.

As seen, the primary software analysis features include energy analysis, thermal

analysis, and lighting/shading. The energy and thermal analysis features take into

account factors such as resource management, heating and cooling loads, and

ventilation and airflow. The lighting/shading analysis tools allow for solar analysis,

right-to-light analysis, daylighting assessment, shading design, and lighting

design. The latest version of the tool allows also for other building facility

assessments such as enhanced acoustic analysis.

The tool is originated for D&Es that would like to get a valuable feedback in the

design process making for creating a low energy building. Recent surveys

indicated that ECOTECT is the most commonly used among D&Es [137].

3.2.2.2.3. Data input

ECOTECT is a highly visual and interactive complete building design and analysis

tool. The user interface for data input is simple and intuitive. It links Designers &

Engineers (D&Es) with a comprehensive and wide range of performance analysis

functions. The user interface tabs that appear on the screen allow the definition of

the BIM model, its visualization and finally its performance analysis in terms of

thermal, energy, lighting, shading, acoustics, resource use and cost aspects. A

snapshot of the user interface is illustrated in Figure 49. ECOTECT has one of the

most user-friendly interfaces that allows powerful visual analysis tool. The

interface is structured around five tabbed views (i.e. project page, 3D editor

page, visualize page, analysis page, reports view page), but navigation and

intuitive usage are restrained by a multitude of options.

It should be noticed that the tool can handle BIM models with any size or

geometry, its main advantage is that it focus on feedback at the conceptual

building design phases. In this context, the tool intents to allow designers to

undertake a holistic approach to the construction product process making towards

delivering energy efficient buildings. Except the internal calculations that

ECOTECT can perform, it allows also the import to a range of more detailed and

focussed building performance analysis tools such as Radiance, ESP-r,

EnergyPlus, and so on.



Figure 49: ECOTECT software overview – workspace, tabs and menu

Concluding on data input, the tool supports a range of data formats for use

alongside most leading CAD and BIM-enabled programs.

3.2.2.2.4. Output

ECOTECT is primarily intended as a conceptual design tool. In this context, the

tool supports the calculation and analysis for a range of building performance

simulation factors. For instance, can display and animate complex shadows and

reflections, it can generate interactive diagrams for immediate overshadowing

analysis, and most important it can calculate various loads (e.g. heat) as well

hourly temperature graphs for any building zone. The tool provides the feedback

to its end-users not only on the BIM models but as well as to a plethora of

graphs, which enable Designers & Engineers to have a better overview of the

building performance in the temporal domain. These are useful to see the

differences in designs, or to compare against reference curves and plots in all

kinds of criteria, thus providing a key functionality to end-users when looking into

improving a building’s yearly performance and seeing where there are problems

and at what times of the year. This can lead to more specific analysis of the BIM

model using a selection of ECOTECT available analysis tools to better understand

the problem.




Strengths

Ecotect really puts something in the hands of architects that empowers better

building design at the early stages of its lifecycle. The main prerequisite for using

the tool is some knowledge of 3D - you can use the built-in modeller, import a

dumb file or utilise CAD data. Moreover, a built-in 3D-modeller facilitates the

construction of the building geometry, but the geometry has to be remodeled

from scratch. User can import 3D computer models in 3DS or dXF formats from

several widely used computer aided design software such as AutoCAD, 3D Studio,

Rhinoceros or SketchUp.

Regarding visualization, the system acts to give great visual best estimates and

can be used to really drill down to generate the best performing design. In

addition, concerning its model viewing capabilities, simulation and performance

analysis results are stored in a single file, resultant graphics are easily

understood, quick and precise result viewing including a zone management

system.

Finally, regarding interoperability, ECOTECT has added the support for IFC and

gbXML schemas, which are the most dominant standards used in AEC industry as

of today.

Weaknesses

ECOTECT does not allow alternatives comparison, code compliance or ranking of

design strategies for different parametric and energy efficiency measures.

Moreover, the tool is not adequate for detailed design, as it does not sufficiently

support input from general to detail and lacks accuracy. The computation time

performance of the tool in detailed BIM models can be excessive and depends

highly on the level of development of the input BIM model.

Despite ECOTECT‘s strength of visualizing output in the 3D-building model, the

results of the thermal analyses (mainly charts), are often difficult to interpret. In

this context, the thermal simulation results are not fully representative of reality.

This is due to the limitations of its thermal simulation engine, which is based on

the CIBSE Admittance Method (CIBSE, 1999).

Moreover, ECOTECT does not sufficiently embrace the Nearly Zero-Energy

Buildings (NZEB) -approach, as it does not assist architects in implementing

renewable energy strategies.

In respect to Adapt4EE, the visualization techniques supported by ECOTECT for

the presentation of the results along with its simple and intuitive interfaces can

establish a reference point for the design and development of the simulation

framework.



3.2.2.3 Integrated Environmental Solution – VE-Ware


The VE-Ware [141] is a plug-in for Google Sketchup and Revit that allows D&Es

to model the whole building energy use and consumption along with its carbon

usage. In general, the software can compare and contrast how different design

choices affect the performance of a wide range of building elements, such as

thermal, airflow, energy consumption and lighting performance.


The software provides a simulation engine that encompasses all the latest cutting

edge simulation techniques, for instance, wind studies, energy efficiency analysis,

airflow and lighting studies. The unique integrated data model enables end-users

to perform faster simulation processing and the analysis on building envelope is

almost complete, taking into account climate and site as well as factors like light,

shade, ventilation, energy, carbon, lifecycle costs, occupant safety and

economics. The tool has in-depth energy analysis by integrating with the Energy

Plus, whereas its performance analysis provides the qualitative and quantitative

data needed to optimise the integrated elements of the design.


The software is a plugin for the Google Sketchup and Autodesk revit tools. The

VE-Ware toolbar (as part of the plugin) in SketchUp software is simple with a

restrained set of options, facilitating data-input and navigation. The processing of

the input BIM-based model is easy and quick, however no customized options are

offered to the end-users. The tool allows the input for HVAC, solar gains, shading,

natural ventilation and dimming strategies. The core of its integrated model is a

3-D geometric representation of the building to which additional information and

data are attached in views tailored for specific design tasks. The integrated model

view allows easy data exchange among diverse CAD applications.

3.2.2.3.4. Output

The VE-WARE plugin software provides an environment for the detailed evaluation

of building and system designs, allowing key stakeholders such as designers,

architects and engineers to optimize them with regard to energy use and comfort

criteria. VE-Ware allows design alternatives comparison and the energy

consumption results can be checked for compliance with LEED and SBEM. The

output results are not very suitable to support the decision-making process. This

is mainly due to lack of visual presentation and too much textual and tabular

information.




Strengths

One of the key strengths of the VE-Ware engine is its thermal analysis engine,

which is based on the incorporation of the ApacheSim framework. The ApacheSim

tool has been widely tested using the ASHRAE Standard 140 and enables the

engine to provide results for a wide range of building performance parameters

(i.e. measures for room performance in terms of air and radiant temperature,

humidity, comfort statistics, natural ventilation, loads and energy consumption,

carbon emissions, etc.)

Weaknesses

The tool from IES is operational as plugin to Sketchup and Revit software. Thus, a

familiar modelling environment to designers and architects can be used to model

the geometry of a building under design. However, the BIM model has to be

imported in the IES simulation engine and this procedure enforces the user to

switch to another working environment. This disadvantage however is rather low

given the fact that the plugin allows directly connectivity to several BIM software

such as Revit, ArchiCAD, fully supporting the import of gbXML and DXF models.

Finally, regarading usability and information visualization, the tool visual interface

lacks of intelligent visualization techniques that could further assist key

stakeholders to get deep insight to the factors affecting the building performance

under design.

Within Adapt4EE, the interfacing of the simulation framework with existing

engines will be of particular interest. As most of the building performance

simulation tools, including IES VE-Ware do not fully exploit the detailed energy

analysis of a building under design due to human presence and movement in its

spaces, the interoperability aspects should be taken into account during the

design and development of the respective engines within the project.

3.2.2.4 OpenStudio3


OpenStudio is cross-platform (Linux, MacOs and Windows) framework that

enables designers and engineers to evaluate the energy performance of buildings

using the EnergyPlus engine. The software is offered as a plugin to the Google

Sketchup drawing tool. OpenStudio is an open source project focusing to its

further development, extension and adoption by the AEC industry. The

OpenStudio plugin toolset contains a graphical user interface along with a

3 Open Studio software, available online at http://openstudio.nrel.gov/



Software Development Kit (SDK). The latest version of the tool, namely

OpenStudio 0.7.0 has been released recently and provides several extensions and

functionalities towards enabling a comprehensive framework for analyzing the

building’s energy performance. A snapshot of the tool is illustrated in the

following figure:

Figure 50: OpenStudio plugin running on the Google Sketchup Pro software


The tool allows the quick creation of building envelope and massing by designers

and architects. It is based on the easy to use and intuitive graphical interface

provided by the Google SketchUp (running as a plugin). The plug-in allows users

to quickly generate geometry and other BIM-related information (e.g. HVAC)

through the available OpenStudio tools (e.g. Graphical HVAC design tool,

RunManager for simulations and workflows management, etc.). It supports

visualization of the simulation results through the ResultsViewer component.

ResultsViewer displays EnergyPlus output in formats that are more useful than

those available directly from the EnergyPlus engine, however the functionalities

provided are still not mature for professional users.


Apart from the OSM (Open Studio Model) the OpenStudio supports several file

formats such as the IDF, the CAD and others formats supported by the Google

Sketchup. gbXML files can only be imported through the Google Sketchup

OpenStudio plugin



Toolbar, whereas export functionality will be addressed in the future releases of

the tool.

3.2.2.4.4. Output

The simulation component of the OpenStudio tool is based on the EnergyPlus

simulation engine. In this context, the tool results fell within the acceptance

range of the ANSI/ASHRAE standard 140-2001.

Figure 51: Snapshot of the simulation results through the Results Viewer tool of

OpenStudio toolset (Source: NREL Open Studio documentation).

Moreover, regarding interoperability, the tool can be mainly used by D&Es and

allow the exchange of the BIM model for more detailed input by simulation

experts.


Strengths

The new suite of OpenStudio tools includes the SketchUp plug-in plus:

� ModelEditor which provides users with a simpler way to edit the building

model. It includes a way to access components that don't have a physical

representation in a building, like a mechanical system (e.g. HVAC

equipment through the graphical HVAC design tool).

� ResultsViewer, a way to review EnergyPlus simulation data in a graphical

format. It allows users to look at the data, draw conclusions and results

analysis by key stakeholders.

� RunManager, which is an application that allows D&Es to run simultaneous

simulations. This powerful tool can be used to run simulations on a

desktop, computer cluster or even a super computer. Designers can



compare results between differing models to see where the best energy

savings can be achieved.

The tool is being developed by the National Renewable Energy Laboratory (NREL)

OpenStudio and is made available to anyone under the GNU Lesser General Public

License, which allows third-parties to easily integrate the functionality into their

applications.

Weaknesses

The OpenStudio software is not a standalone application, thus provides a very

limited database to the end-users in relation to various building entities (e.g.

HVAC elements and construction material are limited). The current version of the

tools does not allow the ranking of different construction characteristics including

internal loads. As already mentioned in the tool capabilities, the tool simulation

output is simple and only basic functionalities are provided. Thus, results in most

cases through the Results Viewer tool are hardly comparable and interpretable by

the users. However, the tool is under active development and several new

features regarding several issues (visualization, knowledge base, interoperability,

etc) will be available in future versions.

3.2.2.5 Bentley AECOSim Energy Simulator V8i4


AECOsim Energy Simulator is a robust energy analysis tool using the EnergyPlus

engine, which could be used with the Bentley Architecture v.8.i BIM models.

Bentley offers this software for building performance design, simulation, and

energy certification based on the EnergyPlus analysis engine and is mainly

targeting in helping end-users to predict a building's real-world performance as

well as to provide required compliance checking and documentation, such as

those mandated by the US Green Building Council’s LEED program.

The software can be used in both design and retrofit work and contributes to the

design of more sustainable buildings that eventually consume less energy during

operation. AECOsim Energy Simulator incorporates the EnergyPlus simulation

engine, the emerging standard for the industry developed by the U.S.

Department of Energy. In this context, the AECOsim Energy Simulator allows

designers to quickly assess design trade-offs in both new construction and

retrofits. It is ideally suited to meet the needs of engineers, building energy

consultants, and designers who need to determine building energy loads and

4 The software is available at Bentley website, http://www.bentley.com/en-US/Products/AECOsim+Energy+Simulator/Product-Overview.htm



explore building HVAC systems and design configuration options by simulating

and analyzing building energy performance.


The software features comprehensive mechanical and energy performance design

capabilities that provide for compliance with industry standards using automated

ASHRAE 90.1 comparisons. This further reduces project risk and results in time

savings by facilitating the certification process. An overview of the capabilities

provided by the energy simulator is illustrated in the following Figure:

Figure 52: Bentley AECOSim Energy Simulator functionalities overview

As seen, the software allows the designers to import BIM models, to quickly

determine building energy loads (through the integrated energy model), to

quickly access designs and explore building HVAC systems and eventually through

different and alternative “What ifs” scenarios to simulate analyze the building

under design energy performance.

3.2.2.5.3. Data Input

AECOsim Energy Simulator allows users to work seamlessly between industry

CAD, BIM, and AEC applications such as MicroStation, AutoCAD, Revit, and others

and supports standard file formats such as gbXML, IFC, DXF, and DWG. This



unique capability eliminates the need to reinterpret the data between different

applications.

3.2.2.5.4. Output

The AECOsim Energy Simulator provides its users with several capabilities

towards evaluating the energy performance of a building with acceptable

accuracy. Users of the tool can create 2D/3D energy models, based upon the tool

can generate reports of peak loads, annual energy calculations, energy

consumptions, carbon emissions, and fuel costs. All of these calculations can

utilize variable runtimes for fast results, and reports are ASHRAE 90.1 standard

compliant.

The tool also includes capabilities for detailed analysis of final-stage models,

enabling prediction of the annual energy consumption, cost, and CO2 emissions

of a building design. It can use real world weather data and actual

wall/partition/glazing construction data, and can take into account complex HVAC

systems as well as the site context, including the impact of adjacent buildings,

when evaluating the energy performance of a design. Regarding its performance

prediction accuracy, it has tools for ensuring compliance with both the ASHRAE

and the CIBSE standards.


Strengths

AECOsim Energy simulator is a part of a large and comprehensive array of tools

and solutions provided by Bentley for building design and performance analysis.

The tools are not limited only to energy efficiency but also space planning

fostering the conceptual design process.

The AECOSim energy simulator is designed to be used with any building model,

not just those created with Bentley’s BIM applications. It can import models in

DGN, DWG, DXF, as well as gbXML formats and convert them into energy

performance simulation models.

Finally, one of the key advantages of the tool is that includes modelling tools for

creating an energy model within the application if required. The tool is available

as a 30-day trial through the Bentley website.

Weaknesses

Bentley tools including AECOsim software continue to remain a very complex set

of applications with a steep learning curve. The quality of the documentation

should be improved towards allowing users to learn the applications, including the

AECOSim energy simulation software.



3.2.2.6 LIDER


The software application LIDER5 is the computer implementation of the overall

verification option of the requirement of limiting energy demand (HE 1)

established in the “Basic Document of Habitability and Energy of the Technical

Building Code” (CTE) provided by the Ministry of Housing and the IDAE (Institute

for Diversification and Energy Saving) of the Spanish Government.

The current software version is v1.0 (July 2009).


This tool is designed for carrying out the geometry, construction and operational

building description and most of the calculations contained in the CTE-HE 1 and

printing of the relevant administrative documentation.

LIDER is designed to define buildings of any size, provided that the number of

spaces does not exceed 100 and the number of elements (external and internal

walls or windows) not more than 500.

If these limits are exceeded, it is possible to divide the building into as many

parts as necessary only to verify the requirements of the CTE-HE 1 and

considered that if all parts comply, the group also meets. If some of them do not,

the user must calculate the average demand of the building and its landmark

building with the calculator PROMEDIAR.EXE included in the directory LIDER.


The input process follows the wizard approach, which is simple and follows the

CTE-HE 1. The interface is kept simple with a restrained set of options, which

improves navigation.

5 URSA Grupo Uralita. (2007). Guia del Manual LIDER Version 1.0



Figure 53: Lider’s software user interface

The program allows three-dimensional visualization of the building as it is being

built. The following sections outline the main functionalities provided by its

toolbars.

Top toolbar

The top toolbar gives access to different modules of the program, they are

ordered from left to right so that they follow definition process of the building.

In the description tab is necessary to indicate the location, the orientation, type

and use of the building and some other general dates of the project as name,

designer, date or owner.



Figure 54: Lider project description form

The BD tab is a database that stores all the information related to materials and

building products that would be included in the building definition.

The form 3D is the core of the geometric definition of the building. In this tab it is

possible to import the drawing plans of the building that would serve as a base to

define the floors of the building, the different elements and its properties.

Left side toolbar

In this toolbar there are the different options to visualize the 3D image of the

building.

3.2.2.6.4. Output

The tool is used to enter construction data (shape, size, orientation, materials,

insulation...) of the building, and the software calculates the energy demand for

the building under study and the reference one to compare it.

After obtaining the results can be seen the verification report of Regulatory

compliance in PDF. This report can be used as administrative justification of the

Standards



meeting.

Figure 55: Lider’s verification of the design requirements in terms of energy efficiency

The ultimate goal of the program is to export the geometric and construction

definition of the building to CALENER to calculate the Energy Building Rate.


Strengths

The tool has a friendly graphical interface and its major strength is the

comparison of reference buildings according to CTE HE1.

Weaknesses

The tool can not be used to validate the energy building rate to international

standards. Furthemore, it is not possible to define internal construction or

geometrically singular elements. To get the BER is necessary to use CALENER,

this software is only a definition step. The computation time is excessive, and the

analysis of a complex building could take hours.

Within Adapt4EE, the intuitive user interface of Lider can be set a reference point

for the developments of the building simulation framework. The visualization of

results and the selection of parameters for assessing design alternatives will be

also of particular interest for Adapt4EE project.



3.2.2.7 CALENER – Energy Efficiency analysis tool


The software tool CALENER is supplied by the Ministry of Industry, Tourism and

Trade through the IDAE (Institute for Diversification and Energy Saving), and the

Ministry of Housing of the Spanish Government, which determines the level of

energy efficiency of buildings.

The CALENER software is the Spanish reference for qualifying the Building Energy

Rating (BER).

The program consists of two tools for easier use by the NT user:

- Calener-GT: Used for big commercial buildings.

- Calener-VYP: Used for medium and small commercial buildings and

residential buildings with simple HVAC facilities.

Calener-GT:

CALENER-GT uses as a calculation engine the DOE-2.2 program developed by the

Energy Department of U.S. Government and the Berkeley Laboratory.

The current version is Calener-GT 3.0 (april 2008).

The current version is Calener-VYP 3.0 (april 2008).


To assign the energy rating, the software compares the annual primary energy

consumption of the building under study (building project) with which would have

a reference building that meets the current regulations:

The Home Energy Efficiency Rating is defined in RD 47/2007 on the basis of the

above comparison, and assigns the values in the range from A to G.

The calculation engine of CALENER, once introduced the required data performs

entire process of analyzing the input building model in terms of its energy rate

mentioned above.


The building to be qualified must have been previously defined in LIDER, and

complies with the CTE-HE 1.

The building preserves the properties from LIDER when opened in CALENER, as

can be seen in the image below, because the toolbars are the same in the two

programs.



Figure 56: CALENER user interface for energy efficiency analysis

In the 3D display shows the building definition and it can be redefined or modify if

it is necessary from the LIDER exportation.

Figure 57: CALENER system view

The only difference from LIDER is the tab system where it can set the HVAC

system. In CALENER a HVAC system is defined with the following objects:

- Systems (information on the set and its properties).

- Facilities.

- Terminal Units (ultimately responsible for providing the final energy required for

conditioning the fitted area).

3.2.2.7.4. Output



The CALENER has a user interface to assess the energy rate of the building under

investigation. After entering the building systems, with their equipment and

terminal units, the software will calculate the Building Energy Rating.

In the interface, a visualization chart will appear with the Building Energy Rate in

a scale from A to G and comparing the building under study with the reference

building according to CTE-HE 1. There is also another tab available that

summarizes the values to the rating given.

Figure 58: CALENER BER estimation and building assessment

Figure 59: CALENER visualization for the summary of the energy consumption and demand of the input building model.



Once the BER has been calculated is possible to get a report of the building in

PDF. This report can be used as administrative justification and include the

Building Energy Rate, as illustrated in the following Figure.

Figure 60: CALENER export of energy analysis in pdf format


Strengths

The major strength of the tool is the provisional comparison of the BER with

reference buildings. The interface is rather simple and friendly to non-expert

users in the AEC community.

Weaknesses

The tool can not incorporate in an efficient manner the facility systems and their

details. The database of the equipment can not be extended and thus the

reliability of the results can not be assured. As the input of the building details

(equipment, material, etc) is getting larger the BER process estimation and

calculation is extremely long.

Within Adapt4EE, special attention will be given to the international standards

regarding the analysis of energy efficiency in buildings. CALENER and other

similar energy efficiency analysis tools will be further taken into account during

the design and development of the Adapt4EE framework.

3.2.2.8 CERMA


CERMA6 [142] is a free software application that allows obtaining the Building

Energy Rate for new residential buildings throughout the Spanish territory. This

tool has been developed by the Valencian Institute of Building (IVE) and the

Spanish Technical Association of Air Conditioning and Refrigeration (ATECYR) with

technical collaboration of the group FREDSOL of the Department of Applied

Thermodynamics of the Polytechnic University of Valencia, and promoted by the

Department of Environment, Water, Urbanism and Housing of the Generalitat

Valenciana.

The current software version is v2.2 (June 2011).

6 CERMA. Manual de usuario. ATECYR, Instituto Valenciano de la Edificación.




The building itself is considered as a single thermal zone, therefore you should

only define the area enclosures that limit heat (to the outside walls, ground, non-

residential premises, or other buildings) and should not define the interior

partitions and the internal floors (although CERMA take this into account in a

roughly way).

The software CERMA includes the following results:

- Demands and consumptions detailed.

- BER detailed.

- CO2 emissions (kg/m2) monthly and annual of heating, cooling and DHW

(domestic hot water).

- Detail of emissions associated with each of the elements of the building.

- Parametric study of improvements in the demands or in the systems or

in combination of both.

- Detailed study to improve the grade obtained.


The input process follows the wizard approach. The interface is kept simple with a

restrained set of options, which improves navigation. It is mainly textual and has

limited visual appearances. It is not possible to see the building designed.

The top toolbar gives access to different tabs, they are ordered from left to right

so that they follow definition process of the building. The first eight tabs (in toal

12) are used to enter the building details:

- Title: This screen contains general and administrative data as the name of

the building, the designer or the certifier of the project. These data do no

condition to obtain the BER.



Figure 61: CERMA software snapshot (first screen)

- City / Environment: In this screen the software user could introduce the

situation of his design and the altitude above sea level.

The new software has to be able to situate your design and define a

climate zone with definite temperatures, radiation and latitude.

Figure 62: CERMA city / environmental screen parameters for energy efficiency analysis

It is possible to locate and mark out the obstacles in the environment of

the building, to know that these cast shadows on the opaque walls of it

(over holes defined in a further screen).



- Global information: Overall description of the building characteristics

(type of building, volume of living space, floor space, number of air

renewals, etc)

Figure 63: CERMA software global information screen

- Description of the walls: In this screen, enter the walls which form part

of the thermal envelope. These can be of several types: external (facades)

and other walls (in contact with floor space which in turn is in contact with

the outside, in contact with the ground, party walls, interior partitions

bordering the areas of the building not heated in buildings).

- Description of the cover: In this screen decks are introduced as part of

the thermal envelope. These can be of several types: external (horizontal

and inclined, both in contact with air) and other covers (in contact with

floor space which in turn is in contact with the outside, in contact with the

earth-covered roofs, adiabatic decks, interior partitions bordering common

areas of the building is not heated in residential buildings).

- Description of the soil: In this screen soils are introduced as part of the

thermal envelope. These can be of various types: contact with the ground

and other walls (outside, in contact with floor space which in turn is in

contact with the outside, forged health, soil adiabatic interior partitions

bordering common areas the building is not heated in residential buildings.

- Description of the holes: This tab groups introduced semitransparent

walls in contact with the outside environment, consisting of windows and

doors covered facades and skylights.



- Description of the facilities: The tool has support for HVAC, Domestic

Hot Water, Lightening, etc.

3.2.2.8.4. Output

Once the building and facilities description has been defined it is possible through the software to calculate the BER for the building under investigation, as illustrated in the following Figure.

Figure 64: CERMA visualization of the BER analyis for the given building model

Apart from the BER the software gives a detailed demand of the different

services: refrigeration, heating and domestic hot water (as also illustrated in the

following Figure).

Figure 65: CERMA detailed demand of the different services.




Strengths

The tool has a friendly user interface and is easier for end-users in respect to

similar tools presented before (e.g. LIDER and CALENER). The final estimation for

BER is given instantly and with high reliability according to standards.

Furthermore, it offers a detailed study to improve the grade obtained by changing

the design parameters.

Weaknesses

The tool is only available for assessing Spanish residential buildings.

3.2.2.9 Energy Building (CivilTech)


Energy Building [143] is an integrated 3D design solution for evaluating energy

efficiency, renewable energy and CO2 emissions in buildings. The tool is capable

to estimate the effect of shading, daylight, ventilation, air-tightness, thermal

insulation, thermal mass, heating and HVAC systems, lighting system and hot

water usage system in the overall energy performance of buildings. Architects

and engineers can process and view the BIM models in 2D and 3D perspectives,

whereas the software provides the functionality to import the BIM model from

well-known AEC software such as Autocad, CADWare, Archicad, Allplan, Revit,

and so on.

Figure 66: Snapshot of the Energy Building workspace provided by CivilTech [143]




The tool offers an intuitive and user friendly interface for the design of BIM

models that can be used for assessing the energy performance of the building.

The tool conforms to the Greek legislation concerning green building energy

certificates and allows D&Es to compare design alternatives for their energy

rating provided by the tool. The tool has been certified also by the corresponding

Greek agency.


The tool can import data from several commercial CAD design tools and suites

including but not limited to AutoCAD, Archicad, Revit and so on. Moreover, the

tool incorporates the 3D Visual BIM technology implemented by Civiltech, in

which the BIM model can simultaneously processed in 2D, 3D perspectives. This

functionality enables users with different experience on building design to

parameterize the building information as well as to effectively visualize its

structural components.

Figure 67: Overview of the 3D model perspective of the Energy Building software (Source: Civiltech Energy Building official manual [143])

3.2.2.9.4. Output

The tool allows architects and engineers to effectively analyze the energy

performance of the building through its energy view tabs. An overview of the

HVAC performance analysis is illustrated in the following Figure:



Figure 68: Energy performance analysis within “Energy Building” tool for internal cooling

and heat loads for the input BIM model (Source: CivilTech official manual [143])

The tool also provides the generation of building thermal/energy zones for better

assessment and analysis of specific spaces of the building envelope. Moreover, it

provides interfacing (via xml output file) with the approved energy certificate

rating software, namely TEE/KENAK, provided by the Technical Chamber in

Greece.


Strengths

The tool has an easy to use interface for manipulating BIM models towards

incorporating the necessary information (lighting, thermal loads, shading, typical

occupancy schedules, etc) for predicting the energy performance of the building

in accordance to the Greek legislation (KENAK - Energy Performance Regulation -

available at www.ypan.gr), which conforms to the Energy Performance of

Buildings Directive published by the European Union (Directive 2002/91/EC).

Weaknesses

The tool is currently only available for assessing Greek residential or enterprise

buildings.



Conclusions on BIM & BPS tools

The commercial tools in the market offer in advance a graphical user interface

and can be applied from the early stages of a design to the commissioning phase,

since the used BIM concepts are equally valid independently of the building life-

cycle. It is evident that every building performance simulation is based on certain

assumptions and approximations that may affect the accurate estimation of the

real building energy consumption during its operational phase. The reliability of

data exchange and straightforward, user-friendly interfaces are major aspects of

the practical usage of these tools. Due to the huge amount of input data and the

availability of rich 3D geometry models effective data exchange and software

interfaces are crucial to enable faster and more reliable energy performance

simulation analysis.

Slow and difficult integration of BPS into design practice has been identified as a

barrier to the use of simulation tools in the AECO industry. In particular, five

obstacles are frequently mentioned concerning the: (1) usability and information

management (UIM) of the interface, (2) integration of intelligent design

knowledge-base (IIKB), (3) integrated building design process (IBDP) (4)

interoperability of building modelling (IBM), (5) accuracy and ability to simulate

complex and detailed building components (AADCC).

Next section provides a depth insight in the BIM & BPS market and outlines

existing findings from recent surveys along with a discussion on future trends in

the AEC industry, in which Adapt4EE framework will put efforts in improving the

current best practices in the topics addressed by the project.

3.2.3 Market Penetration Data

3.2.3.1 The BIM Market

There are several critical challenges towards the full adoption of BIM and its

successful penetration into the market. BIM has been adopted much faster by the

architects’ community, mostly due to clients demand for visual modelling

platforms. Recent years, BIM market penetration into architectural firms has been

occurring three times faster than the market penetration of 2-D CAD in the

1980s, according to Scott Barrington, CEO of former Barrington Architecture &

Design, and founder of BIMstop.com. According to a 2009 PSMJ Resources Survey

(http://www.psmj.com/), 43% of architects using BIM considered themselves

advanced users, compared to 26% in 2007. Still, at present the majority of AEC

experts are not regularly using BIM. Today, architects seem to be the most

frequent users of BIM, on more than 60% of their projects, compared with 43%



of architects who claimed to be BIM users in 2008. In an online survey by the BIM

platform maker Autodesk [180] users of BIM calculated the tools had a return on

investment (ROI) of more than 60%, while 82% of BIM experts believed BIM was

having a “very positive impact” on their company’s productivity, with about 44%

of BIM experts regularly tracking ROI of BIM projects. BIM penetration is also

steadily increasing among other groups of the AEC community. More than 50% of

engineering companies use BIM, though few consider themselves proficient,

based on a recent email survey that involved around 25,000 active members of

the Structural Engineering Institute (SEI) and the Council of American Structural

Engineers (CASE)7. The 1,400 responses were mainly from SMEs of two to 10

people; most structural design firms have fewer than 20 designers.

BIM technology has gained wider acceptance and penetration among the AEC

group in the last decade. A McGraw Hill Construction 2007 Interoperability Smart

Market Report of the building industry8 indicated that BIM was at that time being

used by approximately 20% of designers, while the responses also indicated a

projected usage level of 80% within 5 years and 100% within 10 years. Today’s

statistics are still far from that prediction, however current trends clearly indicate

the potential of BIM having a significant impact on and creating a considerable

opportunity for the AEC community, whether in the design, construction, or

manufacturing side of the industry. Furthermore, there is also a need to convey a

message among the Building Owners and Tenants group concerning the critical

impact BIM can have on the building operations and maintenance needs.

Furthermore, there is a growing trend worldwide to require BIM on even lower

budget construction project contracts. Undoubtedly, one result of this steady

demand increase towards BIM has been the technology innovations on this

domain.

According to an earlier guide of the Associated General Contractors (AGC) of

America (“Contractors’ Guide to BIM-2007”), for projects of low cost even around

$5,000, the benefits of BIM become apparent. While for projects above the cost

of $10-15,000 it is suggested that investment in BIM should be seriously

considered, since the additional costs that may incur by such an approach are

quickly recovered in the short term. Overall, market experience on large

industrial projects indicates that at present the additional cost related to

integrated design represents a 5% to 10% premium on the AEC fees (or roughly

0.25% to 0.5% on construction cost). Immediate savings of a scale around 3% to

7 Report on “BIM and the structural Engineer: Why you need to pay attention” is available at (content.seinstitute.org/files/pdf/BIMandtheStructuralEngineer.6.15.07.pdf) 8 Report is available at www.mcgraw-hill.com/releases/construction/20071024.shtml



7% have been observed due to improved coordination and reduced conflicts. (A

thorough analysis by the Construction Industry Institute www.construction-

institute.org indicated a potential of 7% for this element alone). Undoubtedly, the

ROI will increase as more disciplines are incorporated into the BIM streamlined

process in the future as well as cheaper and easier to use tools will enter the

market.

3.2.3.2 The BPS Market

Building Performance Simulation Technologies have been an inseparable part of

the BIM paradigm. The innovations on this field had a direct influence on the BIM

practice and resulted into four major changes, namely:

• Diversifying tools to address different user needs as well as also serving

the goals of the whole design team

• Delivering a large spectrum of fully fledged functional tools

• Developing tools to capture early as well as late design phases of varying

degrees of granularity and complexity

• Localizing the tools capabilities

The first major change was the trend to encourage the whole design team to use

BPS tools. The increased complexity of building delivery process has led to a

broader view of BPS which resulted in a broader user’s base. Simulation tools

moved progressively towards all professions involved in design of buildings

including architectural designers. Architects, who have been regularly described in

literature as non-specialist, non-professional, non-experts, novice or generalist

([181], [182]) became engaged in the BPS community. Recognizing the

implications of design decisions made by the different team members on the

energy and environmental performance of the building, engaged all design team

members in performing simulations. As a consequence, simulation tools became

recognized as design support tools within the Architecture-Engineering-

Construction (AEC) industry. In fact, simulation became an integrated element of

the design process. This resulted into a diverse growing user’s uptake addressing

more the whole design team. The second major change was the trend to

progressively move towards early design phases. Due to the increasing

importance of the decisions made early in the design process and their impact on

energy performance and cost, several BPS tools have been developed to help

architects perform early energy analysis, and create more energy efficient more

sustainable buildings ([183]). The third change was the rapid sprawl of BPS tools.



Today we have a diverse tool landscape for all building design professionals. The

U.S. Department of Energy (DOE)9 maintains an up-to-date listing of BPS tools on

the Building Energy Software Tools Directory (BESTD) website ranging from

research software to commercial products with thousands of users. In 2010, the

number of tools reached more than 380 tools (US-DOE 2010) whilst today is

more than 400. The number of tools has almost quadrupled in the last decade.

Figure 69 documents the number of developed tools listed on the BESTD DOE for

that period.

Figure 69: BPS tools developed between 1997- 2010

The forth major change was the localization of tools capabilities. With the

localization of BPS tools incorporating local weather data and provision of local

building materials, construction and codes the number of tools users is growing

enormously. High quality thermal models are uploaded on earth viewer software

(Google Earth) and positioned on 2D and 3D satellite images of terrain and cities.

We literally can simply fly over any location on earth and come to a model and

run it using BPS tools. With the rapid advances of computer technology, internet

and building information technology, building simulation will be more often and

more widely applied in building design and analysis worldwide offering design

solutions, economic analysis, comparing & optimizing designs, computing

performance and verifying compliance ([184]).

9 The list of BPS tools in U.S. Department of energy is available at the following location: http://apps1.eere.energy.gov/buildings/tools_directory/



3.2.4 Gap Analysis, Market Success Factors This section includes an overview of the survey results, providing explanations as

well as conclusions on indicated gaps (as realized by D&Es), respective needs and

requirements in the area of BPS as well as proposed future developments that

Adapt4EE should aim at. A detailed overview for user and business requirements

related to the Adapt4EE has been included in the Deliverable D1.1 “User and

Business Requirements” [122].

3.2.4.1 Overall Perspective of Different Stakeholders

Not surprisingly, architects and engineers have different perspectives and rank

differently the relative importance of the main features of BPS tools. This is due

to the fact that even though both disciplines converge in the use of BPS tools,

they have different approaches and goals with respect to specific steps of the

building design process. It is in the core essence of BIM to equally respect the

needs and requirements of both disciplines (among others).

Overall, as indicated in the following figure, the two main groups assigned

different weight to the importance of the different selection criteria:

Figure 70: Ranking the Importance of Features of BPS Tools

Despite the differences between the perspectives of the two groups, there is still

valuable evidence in the relative importance of the different features, as can be

extracted by the combination of survey responses.

3.2.4.2 Usability and Graphical Visualization

Appropriately combining and visualizing all types and levels of information

involved in the building performance simulation process is undoubtedly a heavily

complex task. However, it becomes obvious from existing surveys that the focus

is not on friendly and simple, easy to learn interfaces. Rather, D&E consider more



important the ability to utilize various visualization techniques best fitting the

information at hand (graphs, charts, time-series, comparative reports etc).

Adapt4EE should aim at delivering different views to the BIM information fully

customizable by the different end-users. End-Users should be able to customize

the input parameters as well as the output form of the BPS. Some of the results

and feedback relevant to Adapt4EE are the following:

Feedback from Architects that highlights future challenges:

• Re-usable data input stores and wizard like support in the data entry process

• Defaults templates, but also front-and-center delineation and ability to

create/modify those templates

• Error-checking to ensure models are correct

• 3D visualization of design strategies

• Graphical representation where possible of design parameters (use the

language of architects)

• Balance between extensive (deep) and quickly (basic)

• Ability to evaluate alternative building designs, adding/removing building

features and ability to make custom reports

Feedback from Engineers highlighting additional challenges:

• Streamlining the data entry process and providing guidance by for example

mapping data entry trees and limiting access to relevant paths to objectives

• High levels of customizability in terms of output

• Transparent default options instead of black box approaches currently

provided and more background information

3.2.4.3 Integration of Knowledge Base

As a top priority it has been indicated the ability to perform quick energy analysis

supporting the decision making process at the early design phases. With respect

to the Adapt4EE objectives the main outcome of this part of the surveys

highlighted the need for the development of open, re-usable and fully

customizable repositories of simulation models that could bootstrap the energy

performance evaluation of building designs on specific domains. These models

should allow examining more thoroughly the uncertainty and sensitivity of key

design parameters at the early stage of the process. More specifically, indicative

aspects that could comprise future challenges, as indicated by the different

stakeholders are the following:

Architects’ comments include:

• Scenario/Alternatives based design approach



• Define the most influential design parameters in early design phases and

their sensitivity Assisting decision making process through guidance

• Conform to codes and rating systems

• Contextual material property database

Engineers’ comments include:

• Diagnostics, benchmarking and comparison of results

• Default or built in performance comparisons, benchmarking or ratings such as

Energy Star or LEED

• Multi-objective design optimization

• Recommendations arrived at through an algorithm of combined climate and

building usage

• Introducing optimization models to identify optimal design considering

multiple parameters (performance, cost etc)

3.2.4.4 Accuracy of BPS tools

Surprisingly enough, the degree of resolution of simulation models has been

indicated as the least important factor. Instead, simulation tools and models

should allow quick as well as accurate evaluation of building designs. Future

developments should focus on the delivery of validated performance measures in

order to support sustainable design. More specifically, indicative aspects relevant

to Adapt4EE goals and objectives that could comprise future challenges, as

indicated by the different stakeholders are the following:


• Embodied energy calculation

• Ability to easily simulate essential elements in sufficient detail

• Building envelope design optimization

• Integration of different simulation approaches (daylight, occupancy etc)

Inform users as to the impacts of energy reduction measures


• Real-time results, parametric feedback

• Collecting realistic data from cases- establish performance based data sets

• Data to measure uncertainty

• Adapt to the complexities of the real life designs and climatic conditions

• Indication of the degree of error that could be expected in the results

• Error estimate of models for validation and acceptable error range

• Validation and Verification of the simulation output

• Be built on an underlying database to aid in benchmarking

• Perform trade-off analysis and an LCA tool to compare different options



• Ability to model complex HVAC and lighting control strategies

• Robustness of models. Features should not be added until they are well-

tested features and well-considered

• Describe uncertainty with the data model

• Clarity on the algorithms used to perform the simulations and the limitations

of those algorithms

3.2.4.5 Interoperability

The survey results differentiated between Architects and Engineers in the sense

that both groups prioritized the importance of interoperability with 3D Drawing

and Design tools and MEP tools respectively. As previously indicated apart from

the interoperability between BPS and different major design tools within the

streamlined BIM process, Adapt4EE should also focus on interoperability issues

encountered in the intersection of BPM with BIM. More specifically, indicative

aspects relevant to Adapt4EE goals and objectives that could comprise future

challenges, as indicated by the different stakeholders are the following:


• Allowing input from multiple modelling programs developing complex

geometries

• Importing of detailed geometries with more accuracy and all layers being

correctly imported in energy simulation software

• Proper translation of the geometry and incorporation of necessary features in

complex simulation models

• Ability to change building geometry without having to reenter all simulation

variables from scratch


• One common language like gbXML (but more robust) to become an open

standard, third party organizations need to create a standard language.

• Full IFC compliance: Import / Export equally robust, all elements that can be

modelled must be able to be exported / imported in IFC with all relevant data

• Exchange of model needs to be more seamless and less frustrating which

would greatly facilitate the iterative process of optimizing the design

3.2.5 Conclusions, Future Market Needs, Trends and Opportunities

3.2.5.1 Future Needs, Trends and Opportunities

It is common belief among the AEC community that BIM will prevail over the next

10 years or more, for a number of compelling reasons:



1. Integrated project delivery (IPD), a highly collaborative method that often

includes design-build or design-assist contracting approaches, benefits

greatly from using BIM. IPD spreads risk evenly among project team

members, works out feasibility issues early in the process, and leads to

high-value, cost-effective building solutions. The AGC’s BIM Forum has

focused on IPD over the last two years. The American Institute of

Architects (AIA) documents E202 cover IPD in relation to its BIM protocol.

2. Virtual design & construction (VDC), will be relied on in coming years for

constructability analyses, cost estimating and project scheduling. BIM

supports VDC very well, providing dimensionally accurate 3-D models to

eliminate conflicts among the trades – a process known as clash detection

– and to identify significant discrepancies in modelled and even non-

modelled data.

3. Sustainability and green building. Many firms use BIM to guide the LEED

certification process, and it can be integrated into energy modelling,

airflow analysis, and daylighting studies. BIM also contributes directly to

improved “cradle-to-cradle” project analysis as well as “lean construction”

methods, which are both meant to reduce construction-related waste and

embodied energy. Just-in-time delivery and industrialized prefabrication

are also enabled through dimensionally accurate, information-rich

parametric building models.

The following business and technological trends and consequent opportunities

arise:

Major Business / Market Trends

• Owners are demanding BIM and changing contract terms to enable

its use: Continuously increasing demand by clients and due to project

complexity has boosted BIM adoption by the AEC community. According to

Young [130], in 2009 more than 50% reported using BIM at moderate

levels or higher for “intelligent modelling” as well as streamlining and

coordinating project workflow, compared to a 34% in 2007 and to an

almost zero percent a decade ago.

• New skills and roles are developing while at the same time intense

BIM users among all disciplines involved in construction projects

grew from 34 percent in 2008 to 45 percent in 2009: Leading AEC

firms increasingly recognize that future construction projects will

undoubtedly entail streamlined and integrated practice among various

experts (clients, architects, engineers, contractors and fabricators etc). As



it becomes more obvious that this complex flow can be uniquely and

efficiently facilitated by the BIM practice, new forms of partnerships, with

more design-build projects and more innovative and intensive teaming are

becoming a common business practice. To this end, AEC firms increasingly

invest in the development of HR skills, since ROI as well as necessity for

high expertise becomes more apparent every day.

• Standards efforts are intensified: Continuous Efforts focus in the

design and establishment of standards pertaining to the interoperability

and exchange of information within the full life cycle of construction

projects. Some major standards related to sustainable and energy efficient

designs are presented in Section “2.1.3-Building Data Modelling Standards

and Standardization” of this document. Several other standards have also

been presented in the market by the National Institute for Building

Sciences (NIBS), facilitating industry definition of a set of National BIM

Standards for the US area which aims to precisely specify data exchanges

within specific construction workflows. Furthermore, various industry

groups are introducing “Model View Definitions” as part of this effort and

all major BIM tool vendors now support (e.g. the COBie exchange

standard - Construction Operations Building information exchange, for

handover of equipment lists, product data sheets, warranties, and other

as-built information).

• Green building is becoming a Market standard: BIM facilitates

sustainable design and construction, allowing for the integrated

management of energy related aspects of the building design throughout

the whole project life cycle combined with tools for the analysis of energy

needs and for accessing and specifying building products and materials

with low environmental impact. BIM tools can also assist in the evaluation

of projects for compliance to several related standards and codes (LEED,

Building Energy Rating according to National and EU standards etc).

Increased demand has also resulted in major innovations in the area of

Building Performance Simulation, focusing on various aspects of the

building constructs.

Major Technological Trends

• BIM vendors are introducing new functionality: Major BIM vendors

are adding discipline-specific interfaces and vertical views, objects, design

rules, and functionality to the same base parametric modeling engine.

Mergers and acquisitions in the business arena have also resulted in more



integrated design environments that combine functionality which was

previously provided by multiple vendors (e.g. Ecotect and Green Building

Studio have been acquired by Autodesk, as in the case of many other

major products and tools). Many vendors are also expanding the breadth

of their existing platforms, providing more fully fledged environments,

covering multiple needs and providing discipline-specific BIM tools.

Moreover, construction management functionality and cost management

aspects are increasingly available as a standard BIM tool functionality.

• Re-Usable Building Model Catalogues are provided by various

building product manufacturers: Products as diverse as JVI mechanical

rebar splices, Andersen windows, and many others can be downloaded as

3D objects and inserted parametrically into models from several online

sites. Content libraries such as Reed Construction Data’s SmartBIM

Library, Autodesk Seek, and other similar tools provide large repositories

of building product content for BIM. Content is increasingly accessible

through search engines. Product libraries are primarily developed for the

most common BIM tools, such as RVT file type families, but all are

supported in varying degrees. Libraries providing re-Usable and

customizable design objects for many vertical markets are becoming a

common practice.

• Modular design and coordination of remote collaboration is

facilitated by BIM: Through BIM prefabrication of increasingly complex

building sub-modules is becoming possible at costs and quality that were

previously prohibitive. BIM facilitates the development and exchange of

reliable and error-free data, allowing continuously larger portions of a

building project to be prefabricated offsite, which reduces costs and

increases quality.

3.2.5.2 Conclusions on AEC market trends regarding BIM

Given the fragmented nature of the construction industry as well as its reluctance

to change, it is estimated that BIM adoption will be gradually realized within a

decade’s timeframe. It is a fact that full adoption of BIM in any organization

requires more than two years to become effective and provide the necessary ROI.

Client quality requirements and project efficiency requirements will continuously

become higher; building forms once considered impractical—due to either

technical or budget constraints—will become common. Early adaptation of BIM

will provide benefits and profits to those business experts who will decide to

innovate before competition catches up.



On the mid and long term, there are a number of critical factors, economic,

technological as well as societal, that will strongly influence the future penetration

of BIM tools and practices. These will include: globalization of business services,

specialization and commoditization, international drives for sustainability and the

continuous pressure for energy efficient designs at all phases and construction

levels, and the commoditization of engineering and architectural services; the

move to flexible, streamlined and integrated construction methods, the increasing

use of design-build and integrated project teams; and the demand for high-

quality facility information exchange and management.

Overall, new forms of contracts are becoming the AEC industry standard and will

be further explored in the future, based on Limited Liability Corporations (LLCs).

The Integrated Project Delivery (IPD) approach originally developed in the

United States, efficiently balancing risk and rewards is becoming a part of the

equity relationship between stakeholders involved in the construction projects,

optimally sharing risk, profit and benefits. This new evolution of AEC practice

necessitates for efficient tools and an integrated and systemic approach to the

construction process, which up until today, only BIM has managed to establish.

3.3 Enterprise Business Modelling A lot of different modelling tools exist today due to the broad scope and

application domains, but also within one single domain there are a lot of

alternatives (see Figure 71 [148]). The variety of modelling tools reflects the

need of different approaches and tools for a specific application domain, the need

for different modelling languages and functionalities offered by the tools and the

various types of user profiles.

As part of the evaluation and classification process of current enterprise and

business process modelling tools, annual reports from BPTrends, BPM blogs as

well as research and advisory companies like Gartner and Forrester have been

the main input-sources for this section.

3.3.1 Major Products and Commercial Tools

Within the scope of Adapt4EE we are focusing on modelling tools that are used for

business process modelling, analysis and simulation (see the red bubbles in

Figure 71 [148]). We are not considering complete BPMS systems that

incorporate a run time execution engine as automation is currently out of scope of

the project.



IT Architecture Tools

Knowledge Modeling ToolsIT Architecture Tools

IT Architecture Tools

Knowledge Modeling Tools

Figure 71: Major types of modelling tools, based on (BPTrends, 2007)

In Table 8, an overview matrix of business process modelling tools and their

capabilities that are relevant to the Adapt4EE project is presented.

3.3.2 Future Market Needs, Trends and Opportunities

3.3.2.1 Mobile, Cloud and Social BPM

The current trends and buzzwords in Business Process Management seem to

revolve around new mobile device support and social BPM collaboration potential.

And when we consider doing BPM in the cloud we must address the same

questions about security, cost and integration that come with any cloud

application. But there are additional challenges that cloud based BPM must

overcome before it will become widespread. The main concern at present

surrounds security and risk. On one hand you are potentially trusting a third party

with customer information but on the other hand processes are seen in some

industries as assets that provide competitive advantage so companies can be a

little reluctant to risk exposing sensitive information to competitors.

Many vendors can provide simple mobile functionality such as integrating task

receipt and notifications via email or SMS. But as the use of tablet products such

as the iPad become more widespread then more and more employees will be

walking into their IT departments asking how they can access all their critical

business systems from the tablet device. Therefore IT can no longer ignore the



developing trend in mobile access to enterprise solutions such as BPM, and they

will need to fundamentally change their approach application development and

service delivery.

3.3.2.2 Business Intelligence

One of the strongest trends and perhaps a relevant trend in respect to Adapt4EE

is Business (Process) Intelligence. Vendors currently have strong reputations in

business process design and execution but they are currently not so strong in

proactive trend analysis to be able to tell the system what needs to be done to

achieve a certain goal rather than just providing you with the tools to search for

the problem. There will be more requirements coming from the end users for

stronger reporting, drill downs, KPI management and impact analysis.

In the future together with the Adapt4EE system strong BI could be used to assist

the business analysts in creating energy efficient business processes based on the

energy performance of the environment where they are executed.

Just as in the last few years BPM practitioners will continue to focus on cutting

costs while increasing efficiency. Organizations will strive to combine their

process work together with other technologies to improve business agility and to

deliver a great customer experience.



Table 8: Business Process Modelling Tools and Capabilities

Capabilities

Vendor Product URL

Business Process Modellin

g BPMN

Support Simulation Organizational

Modelling Resource Modelling

Repository/DB Based

XML Model Export

Appian Corporation

Appian Process Modeler

www.appian.com

+ + - + - + +

BOC GmbH ADONIS® www.boc-group.com + + + + + + +

Business Modeler

Business Modeler

www.businessmodeler.com + - - + - + -

Casewise Corporate Modeler

www.casewise.com + + + + - + +

IBM Corporation

WebSphere Business Modeler

www.ibm.com

+ + + + - + +

Software ARIS BPM Modules

www.softwareag.com

+ + + + + + +

MEGA International

MEGA Process

www.mega.com + + + + + + +

Metastorm Metastorm ProVision BPA

www.metastorm.com

+ + + + + + +

TIBCO TIBCO Business Studio

www.tibco.com

+ + + + + + +



3.3.2.3 Building Performance Evaluation and Assessment – Extending BIM with BPM information

Another trend and potential opportunity for Adapt4EE that is developing both in

the research field and with tool vendors in the BIM domain is the possible

extension of their tools to be able to capture and store information related to the

intended practical use and subsequent business performance impact of the space

in the early design phase. The goal is to gather input from various business

analysis experts who may not be experienced or proficient users of CAD/BIM

software so that the planning process is more collaborative between the various

experts involved.

Existing software such as Affinity by Trelligence is available as a plug-in to some

commercial BIM tools that already provide a link between the architects, property

owners and facility managers etc. This link provides the users with a powerful

communication tool whereby the architects can validate their designs against the

requirements of the client and also see the economic ramifications of any

experimental changes to their design. The same early designs are carried through

the project to avoid any re-entry of data and to create efficiencies in the

design/build continuum.

Similarly a real value add to the architect and designers in the early design phase

would be to have an idea of the human flow through the basic space outline that

he is trying to create based on the requirements of the client but also based on

the expected business processes that will be carried out in the space.



4. Recommendations for tools and technologies to be integrated into Adapt4EE architecture

4.1 Introduction The previous chapters of the report provided a deep insight on the available tools,

technologies and frameworks that exist both in the literature and the market.

This chapter provides a series of recommendations and guidelines for the

technologies that could be employed in Adapt4EE.

The project envisages the design and development of a building simulation

framework that will enable key stakeholders in the early design phases of building

to assess its energy performance by fusing two disjoint worlds, the Building

Information Models with the Business Process Models, having as main catalyst the

building occupants modelling and simulation. In this context, the following

sections provide a list of recommendations and guidelines for the tools that will

be designed, integrated and eventually validated during the project lifetime. It

should be noticed that this report provides several guidelines for the

developments that will take place in the project. Thus, the actual choices for the

technologies in relation to several aspects of the Adapt4EE framework will be

finalized in parallel with the process of deriving the overall Adapt4EE system

architecture.

4.2 Advancements in the field of middleware for

accessing data from heterogeneous building

sensors and embedded devices

4.2.1 Choice of Middleware Based on the state of the art results, the following Table 9 summarizes the

middleware approaches to consider during the conceptual design of the Adapt4EE

measurement framework.

Table 9: Summary of middleware approaches based on several criteria and factors

affecting overall middleware usage in diverse application domains including energy

efficiency in buildings

Criteria Middleware Platforms

DPWS LinkSmart UPNP OSGi Kairos

Device discovery yes

devices advertise themselves upon connection

yes

a device discovery manager detects and categorizes devices into

yes

devices advertise themselves upon connection

needs programming

OSGi is a communication layer, hence does not directly detect

needs programming

Kairos only extends existing languages to communicate with WSNs, hence does not





an ontology anything directly detect anything

Device Descriptions

yes

devices are described in XML, notably model name, producer, services, and other device-specific data

yes (also queries via ontology)

devices are described in an ontology where they can be queried according to any of their mapped parameters

yes (no ontology)

device services are listed together with category and other data. There is no inherent ontology manager

needs programming

OSGi is a communication layer, it needs to be programmed to read the descriptions

needs programming

Kairos only extends existing languages to communicate with WSNs, hence does not directly offer description capabilities

Anonymity and Non-traceability

no yes

LinkSmart is developed from the beginning with anonymity and non-traceability in mind. Additionally it offers transparent address changes, IP-mobility, and encryption out of the box

no N/A needs programming

Kairos only extends existing languages to communicate with WSNs, hence does not directly offer anonymity, non-traceability, or encryption capabilities

In use? yes

SIRENA

SOCRADES

AESOP

yes

inCASA

REACTION

BEMO-COFRA

Ebbits

SEAM4US

SEEMPubs

BRIDGE

yes

many devices, supported also by operating systems and routers

yes

LinkSmart itself is based on OSGi. AESPO tries to integrate DPWS with OSGi

not clear

python mappings available

Comm. type SOAP WS SOAP WS

Defaults to TCP; other protocol backbones are available and

SoA

IP Addressing only

OSGi services

JXTA Communication

WS and macros for Wireless Sensor programming





deployable by configuration switches

Wide support for devices

yes yes no yes no

Actively developed?

yes

DPWS was made a standard in 2009

yes

LinkSmart is open source and actively developed by Fraunhofer FIT and other partners

yes

The UPNP standard evolves continuously

yes possibly dated

it was not easy to find references to this project outside of academic papers

Several criteria have been investigated for the selection of the most appropriate

middleware solution. These are outlined in the following paragraphs.

Device discovery

The chosen middleware should be able to discover devices and integrate them in

the system automatically. This allows for adapting the sensor numbers and types

to the ongoing developments in the project. Additionally, this capability allows us

to react fast to the initial findings, without needing to hardcode sensor drivers or

change the software.

Device descriptions

Because of the many types and capabilities of sensors available, the system

should be able to distinguish between them and make the best use of the data

they provide. This can be achieved by having a semantic way of addressing and

communicating with the sensors, i.e. by having the system learn details of the

sensors at runtime, as opposed to programming them beforehand.

Anonymity and non-traceability

The middleware we choose should be able to guarantee such protections to

privacy, so that we can comply with the laws of the countries where the pilots are

(in the short term), and so that the developed systems can be deployed

everywhere without falling foul of privacy laws. We are aware that our sensors

will be almost everywhere and that the data can inherently trace people and

activities, even though this is not the main aim of the measurements. That is why

we want to actively protect anonymity in our measurements.

Middleware currently in use elsewhere



We chose this criterion to give advantage to those middlewares that, on one

hand, have proven to be operational for similar use cases, and on the other hand

are likely to be widely supported and developed in the future, as opposed to

ending up in a niche or stagnating.

Communication type

This is not a "make-or-break" criterion, but it is obviously better if the

middleware can communicate in other protocols in addition to its basic ones, like

SOAP. This allows the middleware to integrate many more devices, possibly even

legacy ones as found in the current buildings. A wider communication range

allows also for deploying the middleware in existing IT infrastructures without

requiring expensive overhauls.

Wide support for devices

We need this in order to be reasonably certain that any sensor we use will work

with the middleware. This is both for cost reasons (allowing for cheaper sensors),

but also for compatibility with existing and new technologies alike.

Actively developed

We want to avoid building a system without a future, or one that will outgrow its

foundations with no hope of being adapted and updated. For this reason we only

consider approaches that are under active development and look healthy enough

to rely on them.

The quite strong candidate approaches are the DPWS and LinkSmart, since they

offer the most capabilities, active support, and extension possibilities. We will

need to work quite intensively with sensors and adapt them to communicate with

the chosen middleware. Hence it is necessary to classify sensor types and usages

along certain dimensions, to draw the requirements for the middleware.

The classification related to sensors and devices can be seen according to the

following dimensions:

Precision

Discrete-valued sensors are those whose output values are clearly separated from

each other.

Continuous-valued sensors are those whose values are real numbers, or discrete

numbers but such that the physical difference between any two successive values

is not readily perceivable by human senses

Need for information



For some sensors, the measured values are usually needed as soon as available

(live, or interrupt-model). For others, the system queries their values only in

particular points in time, or may not need their values at all.

Data flow

Some data make sense as discrete packets - e.g. pictures. Others make more

sense as streams of continuous measurements, e.g. films. In such "stream" data,

the differences between individual readings are as important as the readings

themselves. Table 10 summarizes the classification and gives concrete examples.

Table 10: Sensor classification along three dimensions

Sensor Types

Data Flow Types

Actual need for

information usage and analysis

Packet data Stream data

Publish/subscribe; event on change; data is part of event

Example: presence sensor

Publish/subscribe; A special detector component is needed to detect and fire the event. Subscriber presents detection criteria instead of traditional "topic"

Example: microphone detecting

context from ambient noise

Needed ASAP

Discrete-valued sensors

WS Query, either live measurement or last-read-value

Example: counter of cars in

parking

Subscribe/unsubscribe to stream

Example: (Cardinal) Wind heading

Needed at system's discretion

Publish/subscribe; Subscriber presents frequency and/or amplitude and/or value for which the event will be fired

Example: outside lighting sensor

Stream multicast

Example: video stream

Needed ASAP

Continuous-valued sensors

WS Query with live measurements

Example: water consumption

meter

Subscribe/unsubscribe to stream

Example: atmospheric pressure

sensor

Needed at system's discretion

The two tables above hint that LinkSmart is the better choice for middleware,

both because it measures very well to other middlewares, and because it already

supports most of the sensor communication mechanisms that will be needed

(publish/subscribe and query-based ones). It is already planned that LinkSmart

will be developed to support streaming sensors, so it will shortly support all the

sensor types planned in Adapt4EE. The ontology and device managers are



instrumental in addressing and querying the sensors as they become available.

Furthermore, FIT and TUK have a long experience in developing LinkSmart, which

shortens the time and effort needed and increases the chances of a successful

development and deployment of LinkSmart for Adapt4EE.

4.2.2 Advancements in Middleware

Advancements in the field of middleware in Adapt4EE are twofold.

� First, Hydra middleware concepts of device integration will be applied

and further developed to allow seamless integration of physical devices

and subsystems into the Adapt4EE platform.

� Second, as Adapt4EE has a strong focus on integrating energy

efficiency with business process management and asset management,

Hydra middleware concepts will be further extended to allow

interfacing with such systems. This also includes the development of

Adapt4EE ontology for combining business and asset management

information with energy profile definitions.

The Adapt4EE Middleware will contribute two major aspects of the Adapt4EE

architecture:

� First, the Adapt4EE Device Managers (using and further developing

existing HYDRA technology) will integrate a wide range of devices into

the Adapt4EE system. These include e.g.:

o Wireless sensor networks (WSN) to allow monitoring of

environmental parameters

o Privacy-preserved cameras to allow video based occupancy

modelling

o Other devices that are related to business processes

� The Adapt4EE Ontology and the respective Ontology Manager will

implement semantic device and service descriptions and energy

profiles, so that the Device Managers can provide meaningful

information and semantics-based device access for other components.

� Second, the Adapt4EE Semantic Components will be a bridge between

descriptive data like business processes or building information and

dynamic data gathered by sensors or cameras.

� The middleware will follow the principles of service-oriented

architecture (SOA) to be flexible and extensible with regards to new

devices and open building and business process models.



Furthermore, on this layer, the Multi-Agent Sensor Cloud Management Platform

will reside on this layer. The Agents will represent the various entities ranging

from sensors to enterprise management systems. The multi-agent sensor-

actuator and agent-based enterprise management system will embody the

Adapt4EE Semantic Components in their knowledge frames. These frames will be

extended with context-specific management schemes and mechanisms. Upon

acquiring data from the sensor-actuator networks, occupancy modelling system,

and enterprise management systems, these frames are initialised. Depending on

which entity the agent represents it performs actions on behalf of that entity. All

agents together try to reduce the Energy consumption applying contingent and

robust proactive management schemes in simulation and providing these as

alternative solutions to the enterprise management systems in place.

The sensor cloud architecture provides the means to publish, subscribe and

access to the heterogeneous sensor and actuator networks capabilities deployed

across enterprises for management of their operations, as virtualized services for

enhancing existing and future enterprise management systems in reducing the

energy consumption.

For sake of flexibility and scalability, the sensor cloud is constituted of multiple

clusters (or subclouds) of sensors, grouped together according to flexible criteria

(e.g. location, type of sensor, type of communication technologies, etc.). Figure

72 illustrates the general architecture of the Sensor Cloud and its interface with

the Adapt4EE Middleware.

Figure 72: Conceptual architecture of the Adapt4EE device cloud

Each cluster is coordinated by a concentrator (typically a single board computer

or, in larger clusters, a small industrial PC), which performs the following

functions:



� Communication with the sensors, based on a variety of available

wireless/wired technologies (ZigBee, proprietary 868 MHz wireless, IEEE

802.11, RS-485, X10…).

� Preprocessing of collected data (e.g. buffering, compression, format

conversion, data filtering, event management).

� Basic management of the sensors (configuration and status monitoring,

complementing the functionality of the Adapt4EE middleware device

managers).

� Interfacing with the Adapt4EE Middleware (bridging the gap between the

requirements of the middleware and the effective functionalities supported

by each specific sensor).

The concentrators will be based on the already existing ISA iMeterbox product

line (available in two variants: Industrial and Home), while further development

will be necessary in order to integrate with Adapt4EE middleware.

The architecture is agnostic to the type of sensor and to the specific

communication technology between the sensor and the concentrator. However,

based on the Adapt4EE needs (for the specific requirements of the pilot scenarios

addressed by the project and more specifically the Coimbra Stadium and the

Navarra University Clinic) a number of sensors have already been identified: CO2,

CO, Outside Temperature, Inside Temperature/Relative Humidity, Dew Point,

Small Particulates, Lightning, Motion, Acoustic and Electricity Sub-metering

(outlet, socket and switchboard level). Many of these sensors are already

available off-the-shelf (e.g. using ISA product line), with Zigbee or 868 MHz

wireless communications, while others may require specific integration.

For enhanced coverage, mesh routing is supported (sensors closer to the

concentrator route/forward communications with more distant sensors, thus

extending the wireless range). ISA will also incorporate its energy-saving energy

on battery-operated sensors.

4.2.3 Enhancement in building & device models semantics

Adapt4EE platform will have to deal with a huge amount of real-time information

continually acquired from the multi-sensorial cloud containing various types of

sensors, results of measurements of various physical devices, but also the

changing situation patterns identifying human activity in the environment, etc. All

the information processed by the Adapt4EE platform will be used in the run-time

(measurement phase of Adapt4EE) for training purposes on several levels of

abstraction including multi-agent negotiation, application or business rules.



All of these run-time processes would continually need to access the Adapt4EE

ontologies supporting the information acquisition and analysis in semantic

manner. The semantic models will have to contain the ontologies representing the

common vocabularies shared by all Adapt4EE components to ensure the inter-

component interoperability by representing the all information in uniform,

commonly understandable way. This will require the suitable selection of the

representation ontology language depending on the complexity of the models.

Depending on the formal modelling language selection, taking into account the

amount of information and the fact, that this semantic information may need to

be accessed in the real-time, the proper semantic back-end have to be selected.

The selection of the semantic back-end must also take into account the possibility

further optimization of the query answering, mostly in the terms of speed-up of

accessing the semantic information. Another issue is the requirement of inferring

the new semantic knowledge and integration with the decision making

components and the application/business rule systems.

This setup and criterions lead to the selection of the native triplestore, which is

able to handle the huge amount of semantic data with the possibility of further

query optimization. The recommendation is to use one of the big triplestores with

the strong community support, such as BigOWLIM [99], AllegroGraph [98] or

Jena [97]. All of this triplestores are able to store and access the millions (some

of them also billions) of triples in the real-time. For more, this triplestores support

the complex formal languages such as OWL and implement optimized reasoning

engines. Another useful feature is the support of semantic rule languages as the

support for more complex custom inference of the new knowledge on the fly.

However, the AllegroGraph and Jena support the custom implementation of their

own rule languages, BigOWLIM implements the OWL2RL [187] standard

language.

The semantic rule languages are usually implemented as the static reasoners

inferring the new knowledge only once, when the semantic triples are inserted

into the triplestore. For this reason, these rule engines can't be used for the

decision making in the run-time. There are several possibilities of the run-time

rule languages and reasoners to be used by Adapt4EE decision making

components. The semantic rule languages available include for example the well

know SWRL [188]. However, the potential risk is that the semantic rule

languages have incomplete implementations or the implementations even do not

exist yet. For this reason, a much more better approach is to use some well

known, optimized rule engines with the good community support, such as Drools

[189] or similar. For example, the Drools engine can be easily integrated with the



semantic information, which can be accessed on-the-fly and can be used as the

application rule processor, but also supports standards for business rules

processing, or complex event processing.

As the Adapt4EE platform will have to deal with the huge amount of run-time

information, which will require the on-the-fly semantic support, there is possible

risk, that single instance of ontology management component will be not able to

answer the queries in the satisfactory time. In this case, it will be required to

have an alternative for speed-up of the query answering response. The standard

way is to use the so-called replication clusters, or the federation approach.

The support for federated queries is another possible useful criteria for the

selection of semantic backend. Generally, the federation is an approach, how to

access the distributed knowledge bases usually implemented as triplestores. That

means, the Adapt4EE ontologies can be logically divided into separate expert

knowledge bases, which will be accessed separately in federated way, what will

lead into distributed query answering architecture instead of the single instance

of ontology management component with the riskful response. The federation will

ensure the mechanism enabling to ask the single query, which is broadcasted to

all relevant semantic repositories and the query result is joined as an answer of

only those repositories, which have answered the query. Nowdays, there are

more triplestores, which implement the federation mechanisms, including also the

BigOWLIM, AllegroGraph or Jena. Following the W3C activities, the new

specification of the SPARQL 1.1 protocol [190] proposes the SPARQL language

extension dedicated for design of federated queries. The selection of the

triplestore supporting this standard language is the recommendation. BigOWLIM

and Jena ARQ engine [191] fully support the SPARQL 1.1, AllegroGraph

implements its own federation mechanism.

Moreover, a useful recommendation is related to the process of designing the

ontologies. Based on the lessons learned from other projects, the very important

is to keep the semantic models as simple as possible. That means, instead of

creating the models based on some theoretical assumptions, the good approach

is to design the models following only real requirements based on the need of

concrete processes or domains, which will be really required to be used by

Adapt4EE platform. This common sense approach will help to keep the semantic

models readable, easier extendable, but also more responsive in terms of query

answering time.



4.3 Guidelines for Multi-agent based Modelling and Simulation of Energy Efficiency in Buildings

4.3.1 Role and Objectives of agent-based modelling in

Adapt4EE

Within the context of Adapt4EE project, agent-based modelling and simulation

are tools for making explicit the complex dependencies between building

ecosystem actors and allowing accurate estimation of the energy consumption,

taking whenever information available allows it, occupancy and environmental

factors into account.

The interactions amongst agents representing the different parts of the Building

Ecosystem (assets & facilities, occupants and their activities, the context control

system, the environmental factors contributing to quality of service, etc) impose

certain requirements for an agent-based energy performance modelling &

simulation platform for the building domain.

The responsibilities of an agent-based energy performance measurement and

simulation platform as part of the Adapt4EE system architecture are as follows.

• It is a modular software system that can function independently (i.e., loosely

coupled) of the measurement framework used.

• It supports energy performance parameter calculation using a configurable

Enterprise Energy Performance Model

• It implements the following aspects of building energy performance system:

o energy performance modelling: the impact of changes in actors’ states

on the performance parameters of the building

o occupancy modelling: the effect of occupant groups and their activities

onto the other elements of the building ecosystem, e.g. rooms, energy

consumption, etc

o occupancy simulation: the inter-dependencies between occupant states

and the evolution over time of occupant models

o sensor cloud multi-agent systems: the role of equipment into the

functioning of the building

• It allows configuration of the following simulation scenarios parameters

(through a User Interface or configuration file):

o Assets and facilities provided by the building: size, topology, service

dependencies

o Occupants and their activities: group size, location, schedules, etc

o The Context Control System (operations control)

o The Environmental factors (heat, light, ventilation/wind, energy)



• It supports representation of the different stakeholders of the system,

behaving as rational, goal-aware and utility-aware semi-autonomous actors

o Physical sub-system (buildings assets, facilities and their energy-

consuming equipment)

o Human sub-system (occupants, with their occupancy and usage

behaviour)

o Enterprise sub-system (processes & goals, impact of behaviours, etc)

o General surrounding environment

• It enables agents to employ on-line learning techniques to absorb anticipated

occupancy patterns variability or expected fluctuations in power demand.

During execution of their tasks, these agents apply reactive methods to deal

with unexpected incidents.

4.3.2 The added value of Agent-based building energy performance models and simulation

The interactions between agents representing the different parts of the Building

Ecosystem (assets & facilities, occupants and their activities, the context control

system, the environmental factors contributing to quality of service, etc.) justify a

computational modelling approach which assumes independence of computation

between different parties with a partial view of the “world” (the ecosystem), with

limited resources (i.e., resource-constrained) and with uncertainties arising from

unpredictable changes in occupants’ use of the building (dynamic occupancy

patterns).

Agent-based modelling and simulation can provide clear added value for the

Adapt4EE project. Adapt4EE requires to develop or to reuse tools and algorithms

for modelling, estimation and prediction of occupancy, based on models ranging

from simple models, such as default schedules of occupants, to advanced models,

i.e. occupancy patterns based on spatio-temporal analysis and on technologies for

extracting human occupancy for modelling purposes. Several aspects of energy-

efficiency in buildings need to be modelled using agents:

� The main elements of the Building Ecosystem – rooms, devices, business

activities, environmental factors need to be modelled as energy consuming

and resource providing agents.

� The self-interestedness of agents and their inter-dependencies needs to be

reflected by the agent-based model.

� The possibility to communicate and to coordinate using market

mechanisms (voting, multi-party negotiations, scheduling and planning)



need to be supported by the underlying agent simulation framework

selected to model occupancy patterns.

Agent-based modeling and simulation allows making explicit the complex

dependencies between the actors of the building ecosystem and enables a more

accurate estimation of the energy consumption, taking into account the

occupancy and environmental factors whenever information about them is

available.

The interactions of the following types of agents representing the components of

the Building Ecosystem could be simulated:

� Assets and facilities provided by the building: size, topology, service

dependencies.

� Occupants and their business-related activities: group size, location,

schedules, etc.

� The Context Control System (operations control).

� The Environmental factors (heat, light, ventilation/wind, energy).

To cope with the aforementioned factors, the following aspects need to be

modelled:

� energy performance: the impact of changes in actors’ states on the

performance parameters of the building.

� occupancy: the effect of occupant groups and their activities onto the

other elements of the building ecosystem, e.g. rooms, energy

consumption, etc; the inter-dependencies between occupant states and

the evolution over time of occupant models.

� sensor cloud multi-agent systems: the role of equipment into the

functioning of the building.

Among the capabilities of agents, one is clearly important: they need to be

adaptive by employing some model of their environment and the ability to apply

on-line learning techniques to absorb anticipated occupancy patterns, their

variability and the patterns of control actions that produce energy efficient

building behaviour.

The scope addressed by the Adapt4EE project, i.e. energy efficiency for the

building/construction domain, is particularly complex and raises several

challenges for agent-based approach:

� Diversity of Modelling Programs and supported file formats are not

standardized. A set of Common File formats supporting existing CAD tool

standards is needed.



� Tools not keeping pace with the rhythm of advances in new building

technologies. Therefore, an evaluation of programs, frameworks and

interoperable tools is needed.

� Energy Data used in energy studies is difficult to reuse, extend or

generalize.

Other aspects relate to what needs to be modelled as part of an energy

performance simulation solution. Below an analysis is made of a few of the major

issues which an agent-based approach tries to solve.

4.3.2.1 Partial specification of dynamic behaviour of the

building under design

Not all factors influencing the operating environment of a building can be

specified to an acceptable level. For instance, the external environmental factors,

which influence internal environmental conditions, are difficult to predict and to

estimate, and they depend on the building isolation, location, orientation, etc.

Also occupants do not represent a uniform mix, but can have quite diverse

objectives and unforeseen behaviour.

This makes predicting of the building behaviour over time extremely difficult,

unless the major inter-dependencies and their contributing factors are inventoried

and their impact handled.

4.3.2.2 Limited resource constraints

The Building Ecosystem consists of resource-constrained actors, which have

dependencies on other actors in the ecosystem, via the resources and services

they provide (lighting, heating, etc). The behaviour of these actors is resource-

constrained, semi-autonomous, and self-interested. For instance, the energy

management system of the building has as goals to minimize energy

consumption while maintaining a given environmental quality, while each room of

the building requires energy consuming devices providing heating, ventilation,

lighting, etc to be activated whenever occupants are using the room, while the

occupant expect to be able to perform their activities within acceptable

environmental quality conditions.

All these actors in the ecosystem have limited time to take decisions, and

multiple, potentially conflicting constraints that they need to solve, in their

normal functioning.

4.3.2.3 Partial knowledge of the world

Environmental Uncertainty – unknown occupant behaviour,

unpredictable environment evolution over time



The Building Ecosystem is characterized by the interaction of several categories of

actors: rooms, occupant groups and activities, occupant sensor devices, energy

control devices, environmental factors. The complexity of the occupant groups

and of the environment agent, introduce disturbances in the functioning of the

other, more predictable actors. Thus, in a complex physical system such as that

of a building, current building performance simulation practices investigate

building occupancy with simplified views (based on assumptions) and inherently

incomplete knowledge of the ecosystem they are part of. This is also a

consequence of their resource limitations, and incomplete specifications.

Coordinated Decision making across multiple representation domains and

organizational boundaries

Furthermore, the Building Ecosystem is inhabited by actors using information

from different domains, with limited communication and coordination. The

information is also spread and administered by different organizations. The

increased need for coordination is a result of the dependencies amongst the plans

of individual organizations (stakeholders such as tenants, building maintainers,

designers, etc), each of which has to adapt their plans to the “joint plan”.

Methods to solve the agent coordination and adaptation problem

Within the context of Adapt4EE project, agent-based modelling and simulation

are tools for explicitating the complex dependencies between building ecosystem

actors and allowing accurate estimation of the energy consumption, taking

occupancy and environmental factors into account (whenever information

available allows it).

The main scope addressed by the Adapt4EE project is to find suitable models by

which the main contributing factors to building energy performance can be

represented and to design/evaluate the collaboration mechanisms for different

elements of the building ecosystem to achieve improved energy efficiency for the

building.

� Coordination as constraint satisfaction and optimization problem:

The first approach to solve coordination is to view it as a general

constraint satisfaction problem. The different components of the Building

Ecosystem (building spaces, occupants and their activities, the building

monitoring and control equipment and the environment) impose different

constraints.

� Coordination as scheduling problem: The next step is to view the

constraints placed by the different components of the building ecosystem

as resource constraints. In this case, the coordination problem is



formulated as follows: Can the different actors of the building ecosystem,

self-interested but unable to achieve their goals without collaboration,

achieve near-optimal local coordination, in the form of a global schedule

which is both efficient (e.g., Pareto optimal) and robust? How can these

agents make and coordinate their decisions in order to achieve a globally

efficient and robust schedule in a partially observable and non-

deterministic environment?

� Coordination through agent negotiation: When coordination is

amongst a set of loosely coupled, relatively autonomous and self-

interested entities as is the case in the building ecosystem, then

coordination can take the form of inter-agent coordination, by mechanisms

such as voting, negotiation, auction, etc. Techniques that encourage or

even force self-interested agents to play fairly are:

o Voting: everyone’s opinion counts

o Auctions: everybody gets a chance to earn value

o Contract nets: work goes to highest bidder

For Adapt4EE, contract nets may be used to simulate the resource allocation in

the building ecosystem.

Next sections outline a set of guidelines that should be taken into account during

the definition of the Adapt4EE architecture in respect to the agent-based

modelling framework.

4.3.3 Evaluation Results on existing Multi-Agent Based Modelling and Simulation Tools

Based on the evaluation of existing agent platforms supporting modelling,

simulation and coordination mechanisms such as utility-based negotiation, the

following technologies and platforms are recommended to be used in the

Adapt4EE framework:

� Agent platforms that support domain modelling, and agent behaviour

configuration and modelling: CHAP/Memo(*), JADE, AgentScape, RePast,

NetLogo, MASON, JACK, GENIUS/Negotiator.

� Agent platforms that support ontologies, definition of objectives, tasks,

situations, scenarios: JADE, NetLogo, CHAP/Memo(*), JACK.

� Agent platforms that support Model-Driven Design, Model-Driven

Engineering and Model Transformation: JADE, CHAP/Memo(*), JACK

� Agent platforms that support defining social simulations: NetLogo, Swarm,

JACK, CHAP/Emerge(*), GENIUS/Negotiator



� Agent platforms supporting agent-based negotiations: CHAP/Emerge(*),

Genius/Negotiator

� Agent platforms that support agent development, multiple programming

languages, web-based interface to interact with agents: JADE,

CHAP/Memo(*), CHAP/AIM(*), Agentscape, MadKit.

� Agent platforms that support extension of existing functionality (using

some type of Components-Off-The-Shelf (COTS), such as dynamic

libraries, plugins, web services or other building blocks): CHAP/AIM(*),

AgentScape, JADE, NetLogo

� Agent platforms and tools supporting distributed constraint satisfaction

and resource-constraint scheduling&planning: CHAP/Emerge(*),

CHAP/DEAL(*)

� Agent platforms that support distributed computation, are scalable and

offer a visual, i.e. Graphical User Interface (GUI) for presenting the

Simulation Results: CHAP/Eve(*), CHAP/AIM(*), AgentScape, JADE.

Due to the relative broad type of problems that need to be solved, we

recommend that not one platform, but a set of agent platforms are selected to

support agent modelling and simulation within Adapt4EE. These platforms should

be selected such that they are interoperable, through adapters, semi-automatic

conversions and transformations.

Note(*): CHAP (with its instantiations CHAP/Memo, CHAP/Eve, CHAP/AIM, CHAP/Emerge,

CHAP/DEAL) is an open source agent platform under active development by one of the

consortium partners, ALMENDE. It is recommended that it is considered as an alternative

choice for any new functionality which would be needed for Adapt4EE project. Provided

that the business process/task execution functionality is provided by another existing tool

(e.g., Adonis BPM simulator, jBPM, etc), agent-based models can be employed to perform

energy performance calculations using constraint solving algorithms for resolving

conflicting constraints and/or perform task scheduling. In this context, the agent-based

platform based on CHAP can be considered. Other tools (JADE, JACK, NetLogo) can be

used when this functionality is not sufficient, or when additional scalability and or social

interaction issues need to be studied.

In that case, the necessary mapping of models between CHAP to JADE and/or JACK should

be provided, which both support domain mappings (see [192], [193]).

4.4 Guidelines and Recommendations for Business

Process Modelling (BPM) in Adapt4EE

In section 3.2.1 “Major Products and Commercial Tools”, a literature review has

been made on available BPM modelling tools as well as the underlying modelling

methods supported by the available software on the market. Each method

provides a certain view of the enterprise and some will have advantages and



disadvantages in relation to Adapt4EE. For choosing a method for modelling the

business processes including the Adapt4EE pilot sites, options could range from a

standard simple approach to more complex mapping scenarios:

• One option would be to use a standard method only such as BPMN to

model and describe the process flows of the pilots and extract the skeleton

activity data as required. In this instance it could be difficult to gather all

the data elements required for the Adapt4EE simulation environment if the

required attribute does not exist in the standard method.

• A second alternative would be to extend an existing standard modelling

method with the attributes and concepts required for Adapt4EE. This

would allow the developers to customize a standard method to support the

requirements of the Apapt4EE project and to ensure that the business

modeller is able to record the building related parameters and energy

efficiency data during the modelling process.

• A third option is to combine and extend different concepts and modelling

methods to specify the meta-model required supporting the integration of

the various Adapt4EE framework elements. This would provide the

business modeller with a set of graphical models that can be used to

describe the processes of the domain and capture all the data elements

required by Adapt4EE. It would also be more flexible in terms of the type

of data that can be provided as part of the ‘skeleton’ activities modelled in

the processes.

Finally, special attention shall be given to the instantiation of the enterprise data

models in the domains that will be applied, as different factors may impose

specific requirements that have to be taken into account. In the Adapt4EE,

business modelling approach shall be defined in a way that considers energy

efficiency and performance in mind. The enterprise modelling procedure shall be

able to cope with dynamic building occupancy, thus the incorporation of static and

descriptive data of an enterprise would not be enough for accurate and reliable

performance analysis. The actual business flows (tasks, processes) encountered

in an organization shall be modelled and certain parameters that may affect the

building performance models shall be investigated. Next section provide in more

details several recommendations for the definition of occupancy models in

Adapt4EE that will be able to combine human behavioural patterns, activities and

business models towards providing more accurate prediction on the energy

performance of a building at the early stages of its lifecycle (design phases).



4.5 Guidelines and Recommendations for Occupancy

Modelling in Adapt4EE

The literature review on occupancy modelling, as presented in detail in section

2.2.2 “Occupancy Modelling and Simulation”, revealed the need to further

investigate on the design and delivery of detailed occupancy models that will

allow improving the prediction of energy performance of a building during the

early stages of it design. To cope with this, it is recommended that optimal

occupancy models shall be delivered that will be parameterizable and tailored to

the organizations that will be “housed” in the building under design.

The underlying enriched BIM data models shall enable key stakeholders in the

early stage of the design to perform realistic simulations of human behaviour in

building and based on the generated occupancy schedules to have better

predictions between the simulated and the later real energy consumption of the

building.

The BIM paradigm and the building progressive evolution through Virtual Building

Modelling, as reported in section 2.1.2, denoted also the need to express

occupancy models in an interoperable manner. Thus, within Adapt4EE, special

attention shall be given to the definition and implementation of the occupancy

data schemas and their later usage as part of the dominant standards in the AEC

industry. The enriched BIM data models shall contain the necessary information

to support space utilization and simulation in the buildings under design and

should be able to link BIM information (i.e. spaces, zones, building envelope, etc)

with BPM-related data (i.e. actors, roles, activities, units of an enterprise, etc).

This will allow the effective fusion of two currently disjointed worlds, BIM and

BPM, at the early design stages of a construction product and will enable key

stakeholders, i.e. planners, designers, architects and engineers to perform

enriched building simulations, fully supporting them in the analysis of the future

performance of a building envelope with sufficient accuracy and granularity.

4.6 Guidelines and Recommendations for Visual

Analytics in Adapt4EE

As the AEC industry is becoming increasingly concerned with the energy

performance of buildings, it is realizing that the most important design decisions

that determine the energy performance of the building are made early in the

building design process. Meaningful building energy performance simulation

requires the use of a large amount of data, in which visual analytics could play

significant role in providing the AEC users with the necessary tools and methods

for analyzing different aspects of a construction product. On the early design

phases of a building, linking the BIM models to energy analysis tools with



enriched visualization enables the instant evaluation of energy use in whole

building envelope as well as its internal zones. Focusing on the early design

stages, where the energy efficiency analysis could lead to the delivery of

sustainable solutions, the market survey findings presented in Chapter 3 of this

report indicated that there is a need from AEC community to empower from one

hand the friendliness (i.e. usability and information management) of the building

performance simulation results and on the other to provide data visualization and

graphical representation where possible of design parameters.

To this end, within Adapt4EE, the building simulation framework shall cope with

the incorporation of effective visualization techniques that enable architects and

engineers to get deep insight on the building performance in a concise and

straightforward way, fully tailored to the needs of AEC end-users, as these were

expressed in recent literature and market surveys. With increasing amounts of

information available electronically and as building information models

incorporate more process annotations, information visualization is becoming

central to the overall work process. In particular, founded on a solid multi-

disciplinary basis, visual analytics consolidate the merits of system analytics and

visualization, in order to maximize the efficiency of machine automation

mechanism through the incorporation of human inherent intuition and

background knowledge into the decision making processes. The literature review

on Visual Analytics presented in this report envisaged that existing AEC tools

involve analysis of spatiotemporal data in BIM models and similar visualization

techniques could be also used in Adapt4EE in order to benefit the goals of the

simulation framework that will be designed and developed. The ultimate objective

will be through the information management and visualization of building

performance simulation results to provide key stakeholders the necessary tools

towards better analyzing their sustainable design alternatives, fully exploring the

static and dynamic behaviour of the building under design.



5. Summary and Conclusions The deliverable reports the findings of the literature survey and market analysis

conducted in the context of the Adapt4EE, within the activities of WP1 “Adapt4EE

Definition”. In this respect, the document describes the state of the art in AEC

domain regarding existing tools and methods in several aspects and topics that

are relevant to the research and development that will be performed in Adapt4EE.

One first conclusion is that the AEC market is still a raising one with a number of

diverse and complementary tools available for the AEC users. The introduction of

the BIM models to the early design stages made feasible the evaluation of energy

use in buildings as well as the validation of additional performance indicators such

as space planning, space utilization and occupancy simulation.

The future trends towards the incorporation of dynamic data related to human

occupancy in buildings were presented along with the importance to effectively

utilize data related to the organization that will be “housed” in the building under

design towards reconciling the differences encountered between the simulations

results and the real life.

Modelling and simulating the energy efficiency of buildings and various facilities

semantics has now been established as an integral part of the design process and

many simulation tools are commercially available as a common practice by AEC

users. In this context, Adapt4EE aims to deliver and validate holistic energy

performance models that incorporate architectural metadata (BIM), critical

business processes (BPM) and consequent occupant behaviour patterns,

enterprise assets as well as overall environmental conditions. Thus, within this

report a thorough investigation has been performed towards analyzing the

current trends and future needs for such technologies and frameworks. The

survey findings as well as the literature review conducted will be one of the main

reference points for the developments of respective tools and technologies in

Adapt4EE.

In this context, a chapter has been dedicated in presenting state-of-the-art

technologies that are of particular interest for Adapt4EE scope such as agent-

based modelling and simulation, occupancy & business modelling as well as

semantic-enabled technologies employed in building management frameworks

towards measuring and analyzing the energy performance of building in real-life.

The purpose of semantic-enabled technologies study was: i) to analyze

technologies used to semantically annotate building information related to energy

aspects and ii) to analyze existing tools used for the creation of ontologies and



respective semantic frameworks (middleware) for accessing heterogeneous

sensors and the underlying information data related to energy use in buildings.

The literature review for each of the aforementioned topics includes the analysis

of existing methods and tools used in the AEC industry as well as respective

algorithms and initiatives in European projects.

Moreover, a separate chapter has been included in this report in order to describe

and present in details the latest findings of market surveys targeted at the tools

available in the early stages of design. One major conclusion of the market

survey was that Architects, Designers and Engineers (D&E) lack the tools that will

assist them in the complete evaluation of the energy performance of alternative

design decisions towards producing better and more sustainable construction

products, taking into account all aspects of building operation under real life

conditions. Moreover, an additional remark on research findings was that only

recently the focus was shifted on analyzing the overall patterns, semantics and

complexity of day-to-day human activity and movement within buildings, as well

as the relation of these activities to domain specific enterprise processes

governing commercial buildings operation and performance.

The market surveys revealed also the need to foster the collaboration among key

stakeholders during the progression of construction products by providing

shareable knowledge through the Virtual Building Modelling process.

Interoperability on data interchange will play important role, whereas the

enrichment of descriptive BIM models with information that can express the

dynamic behaviour of a building due to its occupancy will be further investigated

in the future building simulation frameworks.

Last but not the least, the deliverable concludes with a list of recommendations

and guidelines for the tools and technologies that will compose the Adapt4EE

building simulation framework. Special attention has been given in reporting

semantic-enabled technologies that could be employed during the calibration and

training phases of the Adapt4EE simulation models (i.e. occupancy & business

models) along with enterprise modelling software and occupancy simulation

development recommendations. The ultimate goal will be to fully exploit BIM

models enriched with the necessary extensions (e.g. occupancy & enterprise

models) in order to provide critical evidence to simulation frameworks and energy

analysis tools for thorough evaluation of design alternatives.



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