Deliverable D3.3: Simulation Assessment of the PEBBLE ... · In this document simulation assessment studies for the three demonstration buildings are presented. The concept of co-simulation

PEBBLE

Positive-Energy Buildings through Better controL dEcisions

248537, FP7-ICT-2009-6.3

Deliverable D3.3:

Simulation Assessment of the PEBBLE System for the Three Demonstration Buildings

Deliverable Version: D3.3, v.1.0

Document Identifier: pebble_d3.3_simulation_assessment_of_pebble_system_v1

Preparation Date: December 28, 2011

Document Status: Final

Author(s): G. Kontes, G. Giannakis, E. Kosmatopoulos, M. Pichler, H. Schranzhoffer, A. Constantin, R. Streblow, and D.V. Rovas

Dissemination Level: PU - Public

Project funded by the European Community in the 7th Framework Programme

ICT for Sustainable Growth

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PEBBLE, FP7-ICT-2009-6.3, #248537, Deliverable D3.3 i

Deliverable Summary Sheet

Deliverable Details Type of Document: Deliverable

Document Reference #: D3.3

Title: Simulation Assessment of the PEBBLE System for the Three Demonstration Buildings

Version Number: 1.0

Preparation Date: December 28, 2011

Delivery Date: January 9, 2012

Author(s): G. Kontes, G. Giannakis, E. Kosmatopoulos, M. Pichler, H. Schranzhoffer, A. Constantin, R. Streblow, and D.V. Rovas

Document Identifier: pebble_d3.3_simulation_assessment_of_pebble_system_v1

Document Status: Final

Dissemination Level: PU - Public

Project Details Project Acronym: PEBBLE

Project Title: Positive-Energy Buildings through Better controL dEcisions

Project Number: 248537

Call Identifier: FP7-ICT-2009-6.3

Call Theme: ICT for Energy Efficiency

Project Coordinator: Technical University of Crete (TUC)

Participating Partners:

Technical University of Crete (Coordinator, GR); Fraunhofer-Gesellschaft zur Förderung der Angewandten Forschung e.V.(DE); Rheinisch-Westfälische Technische Hochschule Aachen (DE); Technische Universität Graz (AU); Association pour la Recherche et le Développement des Methodes et Processus Industriels - ARMINES (FR); CSEM Centre Suisse d’Electronique et de Microtechnique SA - Recherche et Développement (CH); SAIA-Burgess Controls AG (CH)

Instrument: STREP

Contract Start Date: January 1, 2010

Duration: 36 Months

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PEBBLE, FP7-ICT-2009-6.3, #248537, Deliverable D3.3 ii

Deliverable D3.3: Short Description In this document simulation assessment studies for the three demonstration buildings are presented. The concept of co-simulation necessary for the information exchange between the control design algorithms with the thermal simulation models is presented. Since each of the thermal simulation models is developed using a different simulation environment (EnergyPlus for TUC; TRNSYS for FIBP; and, Modelica for RWTH) differences in interfacing are described. Then for each of the three buildings, for representative control scenarios, a simulation-based assessment is presented.

Keywords: Building Optimization and Control; CAO; Cognitive Adaptive Optimization; Convex Control Design; Thermal Comfort; Control

Deliverable D3.3: Revision History Version: Date: Status: Comments

0.1 12/12/2011 Draft GK, GG, EK, DVR, Structure document, co-simulation and simulation assessment

0.2 23/12/2011 Draft MP, HS, GK: Integration of TuGraz work

0.3 4/1/2012 Draft AK, RS: RWTH Contibution

1.0 7/1/2012 Final DVR, Review, Editing and Homogenization

Copyright notices

© 2012 PEBBLE Consortium Partners. All rights reserved. PEBBLE is an FP7 Project supported by the European Commission under contract #248537. For more information on the project, its partners, and contributors please see http://www.pebble-fp7.eu. You are permitted to copy and distribute verbatim copies of this document, containing this copyright notice, but modifying this document is not allowed. All contents are reserved by default and may not be disclosed to third parties without the written consent of the PEBBLE partners, except as mandated by the European Commission contract, for reviewing and dissemination purposes. All trademarks and other rights on third party products mentioned in this document are acknowledged and owned by the respective holders. The information contained in this document represents the views of PEBBLE members as of the date they are published. The PEBBLE consortium does not guarantee that any information contained herein is error-free, or up to date, nor makes warranties, express, implied, or statutory, by publishing this document.

http://www.pebble-fp7.eu/

Deliverable 3.3Simulation Assessment of PEBBLE Systemfor the Three Demonstration Buildings

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Table Of Contents

1 Introduction 11.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Control Design Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2.1 Cognitive-based Adaptive Optimization Algorithm . . . . . . . . . . 21.3 Validated Thermal Simulation Models . . . . . . . . . . . . . . . . . . . . . 31.4 Scope of this deliverable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2 Simulation Models - Matlab Connection 72.1 CAO - EnergyPlus Connection . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.1.1 Matlab - EnergyPlus Connection . . . . . . . . . . . . . . . . . . . . 72.1.2 Invoking CAO algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2 CAO - TRNSYS (Type155) Connection . . . . . . . . . . . . . . . . . . . . 92.2.1 Co-Simulation Set-Up . . . . . . . . . . . . . . . . . . . . . . . . . . 92.2.2 Virtual experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.2.3 TRNSYS Building Model . . . . . . . . . . . . . . . . . . . . . . . . 112.2.4 Sequence of a Virtual Experiment . . . . . . . . . . . . . . . . . . . 112.2.5 Parallel simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.3 CAO Dymola/Modelica Connection . . . . . . . . . . . . . . . . . . . . . . 122.3.1 MATLAB Dymola/Modelica Connection with BCVTB . . . . . . . 13

3 TUC Building 153.1 Co-Simulation with boundary conditions . . . . . . . . . . . . . . . . . . . . 153.2 Control Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.3.1 Cooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

4 Kassel Building 224.1 Computational complexity and execution time . . . . . . . . . . . . . . . . . 22

4.1.1 Occupancy-profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224.2 VE with night ventilation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

4.2.1 Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234.2.2 Results Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

4.3 Further VE on the TRNSYS simulator and expected energy savings . . . . 27

5 New Building of the E.ON ERC, RWTH Aachen 285.1 Setting-up the Dymola/Modelica simulation . . . . . . . . . . . . . . . . . . 285.2 Adapting the MATLAB routines . . . . . . . . . . . . . . . . . . . . . . . . 305.3 Exemplary experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

5.3.1 Cooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305.4 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

A Rule-Based Controllers and Results 38A.1 Cooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

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List of Figures

Figure 1 Model-assisted control . . . . . . . . . . . . . . . . . . . . . . . 2Figure 2 The building in Kassel simulated in TRNSYS . . . . . . . . . 4Figure 3 Room model with technical equipment and internal gains 5Figure 4 Comparison between the measured and simulated free flow-

ing air temperature in room for the 4th of December . . . . . . . 5Figure 5 Comparison between the measured and simulated free flow-

ing air temperature in room for 5 days of December . . . . . . . . 5Figure 6 Geometry of the simulation model created in DesignBuilder 6Figure 7 Building shading at 21/06 at 09:00 . . . . . . . . . . . . . . . . 6Figure 8 Overview co-simulation. . . . . . . . . . . . . . . . . . . . . . . 7Figure 9 Ptolemy Model for exchanging data between EnergyPlus

and Matlab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Figure 10 CAO - EnergyPlus Connection . . . . . . . . . . . . . . . . . . 9Figure 11 Simulation set-up: Illustration how MATLAB (instance

1) calls TRNSYS and interacts with MATLAB (instance 2) beingpart of the TRNSYS simulation. . . . . . . . . . . . . . . . . . . . . . 10

Figure 12 BCVTB model for the connection of Dymola with MAT-LAB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Figure 13 Dividing the whole building to 3 sub-buildings . . . . . . . . 15Figure 14 CAO connection with the sub-buildings run in parallel . . 16Figure 15 HVAC Setpoints . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19Figure 16 Fanger PPD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20Figure 17 Office 4 behavior for June 13 to June 20 with CAO . . . . 20Figure 18 Office 4 behavior for June 13 to June 20 with CAO-based

rule-based controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Figure 19 Default occupancy-profile for ZUB in Kassel . . . . . . . . 23Figure 20 Weather data for the conducted six virtual experiments. 24Figure 21 Window opening factors for six virtual experiments with

Night Ventilation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25Figure 22 Gains of two optimal controllers for Night Ventilation 26Figure 23 Gains of four controllers for Night Ventilation . . . . . 26Figure 24 Model of a tower with three offices . . . . . . . . . . . . . . 28Figure 25 Results Fanger PPD for an outside temperature of 10 ℃ 29Figure 26 Simulation set-up in Dymola . . . . . . . . . . . . . . . . . . . . 29Figure 27 Outside air temperature for the 6th of July . . . . . . . . . 31Figure 28 Solar radiation on window for the 6th of July . . . . . . . . 31Figure 29 Fanger PPD index values for the two simulations . . . . . 32Figure 30 Cooling temperature set points for the two simulations 32Figure 31 Free flowing air temperature for the two simulations . 32

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List of Tables

Table 1 Sequence of a virtual experiment . . . . . . . . . . . . . . . . . 12Table 2 Decentralized CAO for different thermal zones . . . . . . 16Table 3 State and Action Space . . . . . . . . . . . . . . . . . . . . . . . . 17Table 4 Calculation time in s, and specific calculation time in

s/interval hours . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22Table 5 Rule-based controller vs CAO for June 2010 in Athens . . 38Table 6 Rule-based controller vs CAO for the whole summer of

2010 in Athens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

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Abbreviations and Acronyms

AOC Approximate Optimal Control

BO&C Building Optimization & Control

CAO Cognitive-based Adaptive Optimization

ConvCD Convex Control Design

MPC Model Predictive Control

NEB Net Expected Benefit

NEP Net Energy Produced

SPSA Simultaneous Perturbation Stochastic Approximation

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1 Introduction

1.1 Overview

The building sector contributes significantly to total energy consumption [Perez-Lombardet al., 2008]. The grater part of the energy consumed in buildings is used to operateclimate control devices to foster comfortable conditions in building interiors. Effectivecontrol strategies, which account for weather changes, inhabitants actions and changes ofthe building dynamics hold the promise of reducing the buildings total energy consumption[Giannakis et al., 2011; Oldewurtel et al., 2010]. Especially for existing buildings, theinstallation of a building energy management system along with the necessary sensing andactuation modalities can yield an effective and minimally disruptive option [Ascione et al.,2011].

1.2 Control Design Approach

Recently, Model Predictive Control (MPC) techniques [Goodwin et al., 2005; Maciejowski,2002; Camacho and Bordons, 2004] have been applied to building energy managementsystem design with significant results [Oldewurtel et al., 2010; Gondhalekar et al., 2010;Privara et al., 2011; Kolokotsa et al., 2009; Ma et al., 2011a; Morosan et al., 2010; Privaraet al., 2010; Cigler and Privara, 2010; Ferkl et al., 2010; Siroky et al., 2011; Vasak et al.,2011; Nghiem and Pappas, 2011; Ma et al., 2011b; Coffey et al., 2010]. While MPC methodsare intuitive and relatively straightforward to implement, they require some extra effort,since they necessitate the construction of a simple and accurate mathematical model of theprocess (building).

To that direction, one approach is to use first principle models [Lee and Braun, 2008;Oldewurtel et al., 2010], but for large buildings, the problem is intractable [Privara et al.,2011]. A second approach is to use data-driven models, produced by system identificationmethods [Kolokotsa et al., 2009; Privara et al., 2010, 2011; Cigler and Privara, 2010; Ferklet al., 2010; Vana et al., 2010]. If the identification process is performed on a real, occupiedbuilding, the system might not be exited enough [Vana et al., 2010], leading to inaccuratemodels and poor control design. Trying to avoid this problem, recent approaches [Privaraet al., 2011] use a thermal simulation model of the building for the identification process— which remains a difficult process.

Overall, most MPC approaches to building control use optimal control design techniques,only applicable to linear, time-invariant systems which must be described analytically bya state-space model. As a consequence, the non-linear, time-varying dynamics of buildingsystems are downgraded to linear mathematical models. This can lead to insufficient con-trol designs, since an optimal controller on the reduced linear system does not necessarilycorrespond to a proper controller for the actual building.

Within PEBBLE Project, a co-simulation approach [Nghiem and Pappas, 2011; Coffeyet al., 2010] is developed, surpassing the aforementioned limitations, using a building ther-mal simulation software (like TRNSYS, EnergyPlus or Modelica) instead of an explicitlydefined state-space model, along with an adaptive optimization algorithm and weather andoccupancy predictions for the control design.The overall methodology may be summarizedas follows (Figure 1):

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• As a first step a building thermal model is developed, using thermal circuits, whichconsist of Resistance-Capacitance networks (RC) [Lee and Braun, 2008]. These RCnetworks describe the temporal storage and attenuation of thermal energy throughthe building’s structural elements and, given as inputs weather and occupancy datapredictions for the following day, return as outputs internal thermal conditions. Thegoal of this thermal network representation is to develop an approximate state-spacemodel to be used for designing an initial controller for all building climate-controldevices (HVAC, heater, cooler, etc.). This controller is designed (using an approxi-mately optimal model-predictive control design strategy) to minimize primary-energyconsumption, while preserving comfortable internal conditions (estimated by the eval-uation of relevant thermal comfort indices). Unavoidable are modeling errors, as arethe inputs whose future actual values diverge from the predicted ones. This controllerserves as the entry point for the Cognitive-Adaptive-Optimization algorithm (CAO,Figure 1) which further improves the controller.

• The initial state-space description using the previous RC network is replaced by amore detailed building thermal simulation model, developed using a building thermalsimulation environment (like EnergyPlus, TRNSYS or Modelica). Using the simulatorand weather and occupancy forecasts, an off-line CAO scheme is applied (Figure 1),and the initial controller is further improved and adjusted to the following day’spredicted conditions.

Figure 1: Model-assisted control

1.2.1 Cognitive-based Adaptive Optimization Algorithm

CAO algorithm is the module that optimizes the initial controller and produces the finalcontroller which will be used on the real building the following day. Two versions of CAO



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(CAO I and CAO II) have been implemented and tested [Kontes et al., 2011], varying inthe nature of the cost function.

CAO I tries to minimize the cost function:

min J = w

T∑t=0

tanh(A1E2t +B) + (1− w)

T∑t=0

tanh(A21

N

N∑i=1

F 2it +B); (1)

where Et is the total energy consumption in the interval [t − 1, t]; Fit is the Fanger PPDindex of zone i at time t; N is the number of zones; and w, A1, A2, B hand-tunnedparameters that balance the trade-off between energy consumption and user comfort. Theparameter adjustment can be a laborious and time-consuming task, since the proper set ofparameters depends on the building construction, the location, and the type of sub-systemsused among others.

CAO II on the other hand, reshapes the cost function:

min J =T∑t=0

Et

s.t. C(x, i) =1

T

T∑t=0

Fit −H < 0, ∀ i = {1, 2, . . . , N};

(2)

here Et is the total energy consumption in the interval [t − 1, t]; Fit is the Fanger PPDindex of zone i at time t; N is the number of zones; and H is a threshold value for theconstraint in average Fanger PPD values. This way, the energy consumption is minimized,while ensuring that the average Fanger PPD remains below a certain level H in each zone.

The generality of the later approach stems from the absence of hand-tunning terms. More-over, different constraints — or even more than one constraints — can be used, withoutnecessitating any variations in the cost function or the algorithm.

Global optimization techniques can be used over CAO to avoid entrapment in local optima,but require an excessive amount of simulations that can not be realistically performed, asthe time window for the design phase of the algorithm is limited to few hours during thenight. Moreover, even when a global optimizer is used, the resulting solution requires use ofa gradient-based algorithm, so that convergence to the (possibly) global optimal controllercan be obtained. The CAO algorithm is favored over other gradient-based algorithms,like Simultaneous Perturbation Stochastic Approximation algorithm (SPSA) [Spall, 1998],because it is more sample efficient, has been used successfully to large scale systems, itsperformance does not depend on the definition of proper learning rates, and is easier toimplement. Even for simpler setups, CAO algorithm exhibits superior behavior comparedto the SPSA, while the converse has never been observed. For a detailed comparison, pleaserefer to [Kontes et al., 2011].

1.3 Validated Thermal Simulation Models

Detailed simulation models for the three buildings to be investigated are prerequisites forthe Building Optimization and Control (BO&C) process. Buildings are complex systemsand a detailed simulation needs to take into account the actual climate data, geometries,



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building physics, HVAC-Systems, energy generation systems, natural ventilation, user be-havior (occupancy, internal gains, manual shading) to name but a few. To obtain anaccurate simulation model, detailed representation of the building structure and the sub-systems is required, but it is the integration of all the systems that requires significanteffort. A number of simulation tools are available with varying capabilities — see [Crawleyet al., 2008] for a comprehensive comparison. Within PEBBLE project three such inte-grated solutions are used: the TRNSYS software [Klein et al., 1976], the Modelica languagewith a purpose-built component library for building simulation [Haase et al., 2007], andEnergyPlus [Crawley et al., 2001]. More than one building thermal simulators are used todemonstrate the universality of the developed BO&C system and its independence on thebuilding thermal modeling and simulation software.

The PEBBLE Buildings

• FIBP: The first building is the Centre for Sustainable Building of the FraunhoferInstitute for Building Physics, located in Kassel, Germany. The building is equippedwith a surface heating and cooling system with thermally activated building con-structions. Each office room is equipped with a separately regulated heating/coolingcircuit in the ceiling and in the floor slabs. In addition to these, a number of othercontrol elements, sensors, energy-efficiency and user comfort systems have been orwill be installed. This building is simulated in TRNSYS (Figure 2).

Figure 2: The building in Kassel simulated in TRNSYS

• RWTH: The second building is the new E.ON ERC main building of RWTH AachenUniversity, located in Aachen Germany. The building apart from being a lot biggerthan the other two demonstration buildings is also challenging thermally due to itsmultifunctionality, having both office and conference spaces as well large laboratories,which need to be appropriately climatized. In this case, a good energy performancehas to be assured by a modern building-technology portfolio: gas-powered heat pumptechnology in combination with a geothermal field, low-temperature surface heatingand cooling including concrete core activation, ventilation with heat recovery and asorption supported climatization concept. In addition the building has installed aphotovoltaic array and a heat-recovery system for the server rooms. In winter theexcess heat from the server rooms can thus be used to heat the office and conferencespaces.

This building is simulated using building simulation libraries built using the Modelicalanguage. Figure 3 presents one such model for an office. The model consists of the



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room itself, the technical equipment for air conditioning, CCA and FVU, and internalgains: persons, machines and lighting.

Figure 3: Room model with technical equipment and internal gains

As the building is not yet occupied, there are no actual monitoring data from occu-pied offices or for technical equipment during operation. The validation presented in[Droscher et al., 2011a] is of simulation models developed for other projects, but withthe same modeling-toolbox as the models for the E.ON ERC Main Building. Due tothat, the comparison between the simulation and measurement is demonstrated ona room in an apartment building, which belongs to the “VoWo-Karlsruhe” housingsociety in Karlsruhe-Rintheim and is part of a complex project for analyzing differentretrofit strategies for building built in the 50’s in Germany [Cal et al., 2010]. Aspart of this research project an extensive monitoring system has been installed in thebuilding, allowing validation of the simulation models.

The simulation results for the validation through measured data are presented for asingle, sleeping room with a westwards orientation. Figures 4 and 5 present the freeflowing air temperature resulting from the simulation and measured by the tempera-ture sensor mounted in the corner of the room for one day and five days respectively.A more detailed discussion of the validation is presented in [Droscher et al., 2011a].

Figure 4: Comparison between the mea-sured and simulated free flowing air tem-perature in room for the 4th of December

Figure 5: Comparison between the mea-sured and simulated free flowing air tem-perature in room for 5 days of December



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• TUC: The third building is the Maintenance support building of the Technical Uni-versity of Crete, located in Chania, Greece. In addition to thermal-comfort problemsfor the building users, based on energy audits and simulation results the energy con-sumption of the specific building is quite high at 130 kWh/m2a. The TUC building isunspectacular in most ways and in that sense typical of many existing office buildingsin Greece and elsewhere. It has a glass roof (that can be used for buoyancy-driven nat-ural ventilation) and manually-controlled shading devices and windows. This buildingis simulated in EnergyPlus (Figures 6 and 7).

There are no available validation data for the TUC building, since sensor installationwas infeasible, due to unpredicted problems in the University.

Figure 6: Geometry of the simulationmodel created in DesignBuilder

Figure 7: Building shading at 21/06 at09:00

1.4 Scope of this deliverable

In this Deliverable the cumulative work conducted in WP2 and WP3 is combined: thePEBBLE BO&C system is tested on the full-scale validated simulation model of the realbuildings and the performance of the system is compared to a set of rule-based controllerseither already in use in the real buildings either developed within PEBBLE Project. Theresults presented here, provide conclusive evidence on the efficiency of PEBBLE systemand an estimate on the expected energy savings on the real buildings.



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2 Simulation Models - Matlab Connection

Figure 8 shows the principal approach for the simulation assisted controller optimization.The Simulator represents the building simulation. Every time the CAO algorithm executesa controller test based on the detailed model, the simulator is evoked. This implies executionof one of the three building simulations depending on the building of interest.

Figure 8: Overview co-simulation.

2.1 CAO - EnergyPlus Connection

2.1.1 Matlab - EnergyPlus Connection

Within PEBBLE, control strategies utilizing full-scale (and detailed) thermal simulationmodels, are developed. The effective utilization of such thermal models is based on theestablishment of a dynamic connection between EnergyPlus and Matlab algorithms imple-menting the control and identification strategies. Both for the evaluation of various controlstrategies (i.e. evaluation of the Generation-Consumption Effectiveness Index) as well ascontrol design strategies, hinge on the fact that we should be able to “replay” every daybuilding operational situations at the simulation level. This means that dynamic schedules(for cooling set-point temperature, blinds operation, window operation etc.) have to bepassed interactively to EnergyPlus and then used for the simulation. In the present work,these schedules will be artificial schedules created as part of the learning process of thesimulation algorithms. This model-assisted approach, relying on a simulation-level surro-gate of the real building, allows to test many possible scenarios before the final controlleris created and deployed in the field (for operation of the building the following day).

Such a connection can be achieved using EnergyPlus with External Interfaces and espe-cially with the Building Controls Virtual Test Bed. The Building Controls Virtual TestBed (BCVTB) is a software environment, developed by Lawrence Berkeley National Lab-oratory1. The BCVTB allows simulation of the building envelope and HVAC system inEnergyPlus and implementation of the control logic in MATLAB (or other general purposeprogramming languages), facilitating dynamic data exchange between the two programs,at each time step of the simulation [Wetter, 2011]. The algorithm for exchanging data is

1available at http://simulationresearch.lbl.gov/bcvtb



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as follows: we have a system with two software clients; client 1 being EnergyPlus; andclient 2 being, say, MATLAB. Suppose each client solves an initial-value ordinary differ-ential equation (or a system of such equations) that is coupled to the differential equationof the other client (or any other algorithm for that matter). Let N denote the number oftime steps and let k ∈ {1, , N} be the time-step index. We will use the subscripts 1 and 2to denote the state variable and the function that computes the next state variable of thesimulator 1 and 2, respectively. The simulator 1 computes, for each time step k, the se-quence: x1(k+1) = f1(x1(k), x2(k)); and similarly, the simulator 2 computes the sequencex2(k + 1) = f2(x2(k), x1(k)), with initial conditions x1(0) = x1,0 and x2(0) = x2,0.

To advance from time k to k+1, each simulator uses its own time integration algorithm. Atthe end of the time step, the simulator 1 sends the new state x1(k+1) to the BCVTB andreceives the state x2(k + 1) from the BCVTB. The same procedure is done with simulator2. The BCVTB synchronizes the data in such a way that it does not matter which of thetwo simulators is called first.

To configure the data exchange, the following four steps are required:

• Create an EnergyPlus idf file. It is the input file of the thermal model where we havesupplemented the essential objects of the variables we would like to exchange.

• Create an xml file that defines the mapping between EnergyPlus and BCVTB vari-ables.

• Create an m file to determine the data exchange between MATLAB and the BCVTBvariables.

• Create a Ptolemy model.

Figure 9 shows the architecture of the connection between EnergyPlus and the BCVTBand the connection between MATLAB and BCVTB.

Figure 9: Ptolemy Model for exchanging data between EnergyPlus and Matlab

2.1.2 Invoking CAO algorithm

The Matlab - EnergyPlus connection described above, allows altering the buildings actua-tors parameters (HVAC set-points, window angle, etc.) at each time-step of the simulation.



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Based on that, SimulateAndExit.m file contains a P controller: in each time-step, a set ofcontrol actions is produced, as a linear combination of a set of pre-defined building statesand weather conditions. CAO algorithm produces a set of weights θ (controller) at eachiteration, which is passed on the simulator (EnergyPlus) and receives a real number (J)from the simulator, which reflects the quality of the specific controller. The process is onFigure 10. This way, the simulator acts as black-box, providing only a critic signal onthe quality of the controller, without necessitating any (mathematical) knowledge on theprocess.

Figure 10: CAO - EnergyPlus Connection

2.2 CAO - TRNSYS (Type155) Connection

The CAO algorithm for controller optimization is coded in Matlab and the building modelfor the ZUB in Kassel is designed in TRNSYS. During the optimization process the sim-ulator, is called a number of times depending on the initial controller and the maximumnumber of iterations. This is also indicated in Figure 8 with the inner loop. In the fol-lowing the TRNSYS building model is briefly reviewed, more details can be found in thedeliverables [Droscher et al., 2011b] and [Droscher et al., 2011a]. Finally the realization ofthe co-simulation with a TRNSYS building model is explained.

2.2.1 Co-Simulation Set-Up

The connection between Matlab and the TRNSYS simulator is established using mat-files.In general, there exist a few options for this, but mat-files provide the simplest approachsince the TRNSYS simulation includes an embedded Matlab instance.



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Figure 11 is an overview describing the most important parts of a co-simulation set-upwith TRNSYS. The set-up comprises two major parts: the MATLAB environment (in-stance 1), and the TRNSYS simulation simulation.dck, represented by the upper left andlower box, respectively. MATLAB (instance 1) includes the CAO algorithm (the opti-mization algorithm), a parameter file and the simulation call. The TRNSYS simulationembraces weather data supply, TRNSYS Type155, and the main building model (Type56-TRNFLOW) including basic parameters such as geometric and wall definitions. Type155represents the embedded MATLAB environment, indicated as (instance 2). More detailsabout the building model are given in subsection 2.2.3. Data exchange between the twoMATLAB instances is realized by means of mat-files shown in the upper right part. Thisdata communication requires a handshake mechanism for synchronization. More details onthe communication and the functionality of the individual parts is provided in Table 1.

Figure 11: Simulation set-up: Illustration how MATLAB (instance 1) calls TRNSYS andinteracts with MATLAB (instance 2) being part of the TRNSYS simulation.

Establishing the two-way interaction whereby dynamic schedules created from the CAOalgorithm are passed to TRNSYS is a prerequisite for the development of the control strate-gies. A second requirement is the polling of weather forecasts so that weather files can becreated for the simulation.

2.2.2 Virtual experiment

The procedure of parameter search for certain controllers being part of the building isnamed a Virtual Experiment (VE). An experiment is conducted for a certain predictionhorizon.

In a typical control design scenario the CAO algorithm calls the TRNSYS simulator a fewhundred times during one experiment, each time to simulate for the prediction horizon ofone day — of course, the number of times TRNSYS needs to be invoked hinges upon thecomplexity of the building and the starting initial guess for the controller.



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2.2.3 TRNSYS Building Model

The current TRNSYS building model, the Tower, is essentially a cut-out of the originalbuilding model for the whole building. This was done to reduce the complexity and alsothe calculation time. The Tower model comprises nine thermal zones. The essential partsof the building model are shown in the lower box of Figure 8.

The main physical model Type56-TRNFLOW comprises two kinds of elements, the buildingmodel itself — passive devices such as walls and roofs — and active devices such as TABSand operable windows. A few zones have only passive devices but some have passive andactive devices. Controls related to such active devices, and the user comfort modeling,require input variables. These are either given as constant values or as input-variables tobe supplied by Type155. Active and passive devices from Type56-TRNFLOW generatenumerous output variables.

The mentioned controls could be assembled in TRNSYS using a number of Types for eachactive device. Although similar control tasks might exist, each active device requires its owncontroller. This fact is unpleasant for flexibility and an extension of the model. Vector andmatrix elements in MATLAB provide an elegant way to solve multiple but similar controltasks. Because MATLAB provides the optimal tool for these control tasks, a MATLABinstance embedded in TRNSYS is used, depicted as Type155-control in Figure 8. Theemployed modular approach allows for quick extension or modification of the building model— details on this topic are described in [Droscher et al., 2011a]. To summarize, Type155-control provides certain building parameters, supplies schedules and fulfills control actionsexpected in reality also from the real building. Supplied schedules include occupancy profiles(Figure 19) and internal gains, window or mechanical ventilation operation, external shadingcontrol, to mention but a few.

Initial airnode temperatures are set to 15, 16, 18 and 20 degree Celsius, for the thermalzones SOIL_LOW, SOIL, CELLAR and all office zones, respectively. The initial relativehumidity is set 50% in each thermal zone. The building time constant reported in [Droscheret al., 2011a] is between approximately 160 and 250 hours. This time constant is relevant todetermine an ideal duration of the settling phase used for model-data assimilation. However,applying certain tricks it might be possible to reduce the duration of settling, dependingalso on the availability of measurement data for the real building.

2.2.4 Sequence of a Virtual Experiment

Table 1 shows the calling sequence and data communication during one VE. There areessentially two major columns, the left one refers to activities in MATLAB (instance 1)and the right one shows tasks conducted at the Simulator, simulation.dck and in MATLAB(instance 2), compare with Figure 11. Anything related to the estimator is neglected in thefollowing description.

A VE starts with the execution of CAO_algorithm.m, which calls Parameters.m, wheredefault parameters for the simulator are set. The next step is the execution of the simulatorto test a given controller θj . Before the main building simulation for the prediction horizonoccurs, data assimilation is realized using measurement data from the real building. Controlsetpoints are calculated by the product of the selected exogenous variables with θj . Whentested on the simulator, each controller is employed for a complete simulation intervalexclusive a prepended settling phase. After this, the cost function value Jj resulting from the



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controller θj is returned to CAO_algorithm.m MATLAB (instance 1) for evaluation. Thisprocedure starts again for a different controller, until the maximum number of iterations isreached.

Table 1: Sequence of a virtual experimentMATLAB (instance 1) Simulator and MATLAB (instance 2)Run CAO_algorithm.m↪→ for j=1:max iteration .. .. set default parameters. polling occupancywait polling weather data. Call Parameter.m. set default parameters. data.mat-file −→ awake simulation.dck. data assimilation. normalize variables. load θj. for i=1:interval

wait calc. control setpoints(i). deploy control setpoints(i). next i

. calc. cost function Jj

←− data.mat-file return Jj for θjevaluate θjnext j

Provision of occupancy and weather data is crucial, these data are provided before each VEto be accessed from the simulator.

2.2.5 Parallel simulations

The controller optimization process with CAO requires a number of virtual experiments,each of which consists of up to a few hundred simulations. It would be desirable to runvirtual experiments in parallel on a multicore CPU to speed up the controller optimizationprocess. This would mean parallel building simulations.

The used Type155 provided by the distributor of TRNSYS is programmed in such a way,that only one MATLAB instance can be evoked from any TRNSYS *.dck-file at a time.This does not allow simple parallel building simulation runs on a multicore CPU.

There are a few ways to solve this problem. Asking for a version update of Type155; this hasalready been done. Trying to avoid the embedded MATLAB in TRNSYS; this would entaila huge work load. Finally, running each simulation on a virtual machine would be anothersolution, but therefore for each VM an additional TRNSYS license would be required. Inconclusion, a version update for Type155 would be the most practicable solution.

2.3 CAO Dymola/Modelica Connection

The new E.ON ERC main building has a very complex energy concept, which proves to bechallenging to model accurately as a whole building. Furthermore, a successful implemen-tation of PEBBLE should allow it to actively change certain parameters in the control ofthe technical equipment. This is why we decided to test the PEBBLE concept on the utility



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area of office rooms, by allowing PEBBLE to set the temperature set point at the FVU.Based on the experience of our project partners from the TU Graz, we decided to buildtowers of such office rooms, by cutting out this utility area from the building and developCAO controllers for every type of tower and by cross-testing the different controllers witheach type of towers to ascertain how many different types of controllers are needed. Theparticularities of these towers and the technical equipment are described in [Droscher et al.,2011a]. Also the description of the MATLAB Dymola/Modelica connection was expendedfrom [Droscher et al., 2011a].

2.3.1 MATLAB Dymola/Modelica Connection with BCVTB

The CAO algorithm is implemented in MATLAB. The building model is written in Modelicaand the simulation runs in Dymola. In order for the CAO algorithm to communicate withthe simulation a connection between Dymola and MATLAB needs to be set up. To thispurpose BCVTB was used.

BCVTB is a software environment that enables co-simulation, by coupling different simula-tion programs, in this case the building model in Dymola with the “controller” in MATLAB.

The main routine is the MATLAB-routine that determines the best controller for the build-ing. The controller is determined on a daily basis, meaning one controller for each day. CAOproduces and tests multiple controllers with the help of the simulation, in the end selectingthe best controller. Each of these controllers is tested via simulation for one whole day. Sothe simulation is only a secondary part of the selection algorithm. The simulation startsthrough a system call from MATLAB, which opens BCVTB and runs the simulation forone day, giving back relevant data to the CAO algorithm, which in return generates a newset of controllers.

Figure 12 shows the BCVTB model for the coupled simulation. The simulation is set for onewhole day (24×3600 s), and the rate of data exchange between the two models is every tenminutes (600 s). Although the controller is set for the whole day, the communication intervalof every ten minutes is necessary in order to set new set points, e.g. for temperature. Thecontroller is actually a series of weights that are used in setting this set point temperaturedepending on other variables, as for example the outside temperature. In this way everyten minutes new set points are calculated. The ten minutes interval is considered adequate,as the reaction time of the FVU itself, is shorter than ten minutes. At the present timearound three minutes, meaning the controller of the FVU has time to react to each new setpoint independently.



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Figure 12: BCVTB model for the connection of Dymola with MATLAB

BCVTB connects the two models by running two scripts, aptly entitled “simulateAndExit”.The coupled simulation starts with the Dymola script.

The scripts supported by Dymola are called “.mos”-Files. Dymola also allows for the modelto be saved in an independent library along with all its components, making the script com-pact and in this way shortening the overall simulation time in case of repeated simulation,as only one library needs to be opened. Changes in the model are however not possible inthis form, so each time the model is adapted; it has to be saved again in this way.

The code for the simulation of the tower is listed below. The simulation command is sim-ulateModel(). Each parameter apart from the model name marks a change of a defaultsetting. The default simulation time is 1 s, and in the present case a whole day was simu-lated, 86400 s, starting with second zero, meaning the 1st of January. The exit() commandcloses the program.

openModel(“OneTower_BCVTBTotal.mo”);

simulateModel(“EON_ERC_MainBuilding_Simulations_OneTower_BCVTB”, stopTime =86400);

exit();

The script in MATLAB is a simple “.m” - file. The “simulateAndExit.m” file is morecomplex in MATLAB, as the error handling and simulation is done here explicitly. Theparticularities of this file, for the MATLAB Dymola are detailed in [Droscher et al., 2011a]

A simulation with 100 iterations lasts for about 30 minutes. Further research is necessaryin order to make sure that the simulation runs smoothly, as MATLAB, seems to get stuckat certain points, and the simulation has to be set forward manually. However it seems toonly be a problem when other programs are also running on the computer, so simulationsshould be done on a computer used exclusively for this purpose.



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3 TUC Building

Although a detailed model of the TUC building is designed in EnergyPlus [Droscher et al.,2010], experiments show [Droscher et al., 2011a] that requires high execution times — evenafter applying speed reduction techniques, such as the use of ideal HVAC systems andsimplified geometry —, which makes it inappropriate to use within the PEBBLE BO&Csystem. For that reason, a radical model reduction is applied, dividing the whole buildingmodel into sub-buildings.

3.1 Co-Simulation with boundary conditions

The whole building is divided to three sub-buildings, presented in Figure 13.

Figure 13: Dividing the whole building to 3 sub-buildings

With this method, and by properly defining the intercommunication between the sub-buildings using bcvtb (Figure 14), the three sub-buildings run in parallel, without com-promising the accuracy of the boundary conditions [Droscher et al., 2011a]. As shown inFigure 14, CAO algorithm is executed in the main Matlab file, providing the controller, andthe appropriate control decisions, along with the proper boundary conditions, are passedto the sub-buildings in each time-step of the simulation.



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Figure 14: CAO connection with the sub-buildings run in parallel

3.2 Control Design

In [Kontes et al., 2011], a centralized controller, containing three office rooms is defined.As we are moving towards real buildings, the size of the centralized MPC problem rapidlygrows due to the “curse of dimensionality [Bellman, 1966]. In TUC building, a centralizedcontroller has to take into account 10 zones, when the interaction between sub-buildings 1and 3 can be neglected without any loss of accuracy. With this centralized approach, CAOalgorithm demands a high number of iterations, since the best solution for all the zonessimultaneously is required.

In an effort to minimize the computational burden, a decentralized MPC algorithm [Maet al., 2011b; Morosan et al., 2010] is implemented. Here, sub-buildings 1 and 3 are consid-ered independent and are divided into two thermal zones each — with one CAO algorithmfor each thermal zone —, as shown on Table 2.

Table 2: Decentralized CAO for different thermal zones

Controller OfficesCAO1 4, 5-6CAO2 11, 13CAO3 1, 2, 3CAO4 8, 9, 10



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Each thermal zone is controller by a P controller, which takes into account all the states ofthe neighboring offices, as in [Giannakis et al., 2011; Kontes et al., 2011]. More analytically,the state and action spaces (X ∈ R23 and U ∈ R45) are as follows (Table 3:

Table 3: State and Action Space

ID States Actionsx1 → Outside Dry Bulb

Weather x2 → Outside Humidityx3 → Global Solar Radiation

x4 → Zone Mean Air Temperature u1 → Heating Set-pointOffice 4 x5 → Humidity u2 → Cooling Set-point

u3 - u5 → Window Openingx6 → Zone Mean Air Temperature u6 → Heating Set-point

Office 5-6 x7 → Humidity u7 → Cooling Set-pointu8 - u11 → Window Opening



Office 13 x11 → Humidity u19 → Cooling Set-pointu20 - u21 → Window Opening










Assuming the column vectors X1 = [x1, x2, x3, x4, . . . , x7], X2 = [x1, x2, x3, x8, . . . , x11],X3 = [x1, x2, x3, x12, . . . , x17], X4 = [x1, x2, x3, x18, . . . , x23], U1 = [u1, . . . , u11], U2 =[u12, . . . , u21], U3 = [u22, . . . , u33] and U4 = [u34, . . . , u45], the full building controller will



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be:

U1

U2

U3

U4

=

θ1−1 . . . θ1−7 0 0 0...

. . ....

......

...θ11−1 . . . θ11−7 0 0 0

0 θ12−8 . . . θ12−14 0 0...

.... . .

......

...0 θ21−8 . . . θ21−14 0 0

0 0 θ22−15 . . . θ22−23 0...

......

. . ....

...0 0 θ33−15 . . . θ33−23 0

0 0 0 θ34−24 . . . θ34−32...

......

.... . .

...0 0 0 θ45−24 . . . θ45−32

×

X1

X2

X3

X4

Moreover, a different, more strict cost function than (2) is defined. (2) forces the averageFanger PPD index of each zone to remain below a threshold H. This can result to solutionswhere at some point during the day Fanger PPD is high, but the average remains belowthe threshold. Here, a new cost function, which forces Fanger PPD to remain below H ineach time-step is used:

min J =T∑t=0

Et

s.t. C(x, i) =T∑t=0

Vit = 0, ∀ i = {1, 2, . . . , N},

with{

Vit = 1, ifFit > HVit = 0, ifFit ≤ H;

(3)

here Et is the total energy consumption in the interval [t − 1, t]; Fit is the Fanger PPDindex at zone i at time t; H = 15%; and N is the number of zones. This way, the energyconsumption is minimized, while ensuring that Fanger PPD remains below a certain levelH in each zone.

3.3 Experiments

A set of experiments is designed to determine the efficiency of decentralized CAO algorithmin different time-periods: winter with only heating available, summer with only coolingavailable and spring with both cooling and heating availability, using a meteonorm [Remundet al., 1999] validated weather file, from an area with similar weather conditions with Chania



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(Athens). For simplicity all rooms are occupied by one user from 08:00 to 16:00. Eachcontroller produced by CAO, is compared with a simple, widely-used rule-based controlstrategy.

3.3.1 Cooling

A rule-based controller is defined and applied for the whole June 2010 , as shown in TableA.1. Subsequently, decentralized CAO is applied for the same time interval and the resultingHVAC schedules and Fanger PPD values for 8 days (June 13 to June 20) are in Figures 15and 16 respectively.

0 200 400 600 800 1000 1200 1400 160022

23

24

25

26

27

28

29

30

Time in 10 min

HV

AC

Set

poin

t

Office 4Office 5−6Office 11Office 13Office 1Office 2Office 3Office 8Office 9Office 10

Figure 15: HVAC Setpoints

The controller produced by CAO results in 14.3% − 15.7% energy savings, for the sameamount of violations, compared to the rule-based controller, or — from a different point ofview — to 24.4%− 25.2% less violations for the same energy consumption.



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0 200 400 600 800 1000 1200 1400 16000

10

20

30

40

50

60

Time in 10 min

Fan

ger

PP

D

Office 4Office 5−6Office 11Office 13Office 1Office 2Office 3Office 8Office 9Office 10Occupancy (scaled)

Figure 16: Fanger PPD

This results, from the dynamic set-point schedule produced (Figure 15) in contrast to thestatic HVAC set-points of the rule-based strategy.

0 200 400 600 800 1000 12000

10

20

30

40

50

60

70

80

Time in 10 min

Val

ues

over

the

day

Zone TemperatureHVAC set−pointsFanger PPDOccupancy (scaled)

Figure 17: Office 4 behavior for June 13 to June 20 with CAO

Figure 17 offers a closer look on Office 4. It is obvious that CAO algorithm resulted to amore “sophisticated” rule-based system, since the cooling set-points remain (approximately)the same during the day, but are different between the zones and from one day to another.

This is due to the fact that up to this point the building is designed, according to theinitial plans, without substantial insulation. This hinders the overall optimization process,indicating that an “intelligent” rule-based controller covers this type of buildings.



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To that direction, a new optimization setup is defined. The overall process remains thesame, but the controller now is not a P controller, as in 3, but a simple controller, defininga constant cooling setpoint for each zone throughout the day:

[u1, . . . , u10]T = [α1, . . . , α10]; (4)

where αi ∈ {22, . . . , 30} ℃.

The performance of this newly-defined controller is in Table 5. It exhibits higher perfor-mance, compared to the setting with P controller (24.6% energy savings, compared to theoriginal rule-based controller for the same (lower) amount of violations), due to lower com-plexity and available good initial controller (e.g. 25 ℃). Figure 18 shows the behavior ofCAO-based rule-based algorithm for Office 4 and for June 12 to June 20 (as in Figure 17).

0 200 400 600 800 1000 12000

10

20

30

40

50

60

Time in 10 min

Val

ues

over

the

day

Zone TemperatureHVAC SetpointsFanger PPDOccupancy (scaled)

Figure 18: Office 4 behavior for June 13 to June 20 with CAO-based rule-based controller



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4 Kassel Building

4.1 Computational complexity and execution time

The computational complexity and the total calculation time of a virtual experiment de-pends on a few things: the settling interval, the length of the prediction horizon, the numberof iterations, and of course on the principal calculation time of the building model. Modelcomplexity and calculation time is also dealt with in deliverable [Droscher et al., 2011a].

Table 4 provides information on the total and specific simulation time for two simulationswith different simulation interval. The left column gives the Time Step, the second and thethird column list the calculation time for Type56_TRNFLOW and Type155, respectively,and the last column gives the total calculation time. As can be seen clearly, Type56 andType155 account for the major shares on the total calculation time. The Time Steps0.25 hours and 0.5 hours are the favorite values for application. The specific calculationtime e.g. 0.139 s/hour for a time step of 0.25 gives the mean calculation time required forType56_TRNFLOW to simulate an interval of 1 hour. That is, the specific calculation timeenables the estimation of the total calculation time for other intervals. It has to be noticed,that the specific calculation time is similar for the two intervals for Type56_TRNFLOW,but different for Type155.

These results were obtained on an office computer Intel(R) Core(TM) 2 Duo CPU 2.66GHz, 4.00 GB RAM; OS Win7 Enterprise 64 Bit. To extrapolate the results from thetable for other CPU’s this has to be taken into account. Assume a total simulation intervalof 168 hours including the settling phase, the total calculation time of one iteration isapproximately 70 or 170 seconds depending on the Time Step. The total calculation timeof a virtual experiment scales linearly with the number of iterations.

Table 4: Calculation time in s, and specific calculation time in s/interval hoursTime Step Type56_TRNFLOW Type155 Total

Total simulation interval 96 hours0.125 hours 23.01/0.240 178.65/1.861 203.2/2.1170.25 hours 13.35/0.139 65.24/0.680 80.05/0.8340.5 hours 8.45/0.088 27.36/0.285 37.16/0.387

1.0 hours 5.71/0.059 13.12/0.137 19.95/0.208Total simulation interval 168 hours

0.125 hours 40.36/0.240 449.37/2.675 491.65/2.9260.25 hours 23.33/0.139 145.33/0.865 170.27/1.0140.5 hours 14.14/0.084 53.57/0.319 69.19/0.412

1.0 hours 9.51/0.057 22.97/0.137 33.7/0.201

4.1.1 Occupancy-profile

Figure 19 shows the default occupancy profile for the building in Kassel, which was usedfor the following experiment.



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Figure 19: Default occupancy-profile for ZUB in Kassel

4.2 VE with night ventilation

Night ventilation in summer is a useful method to reduce the temperature of the buildingthermal masses and hence the cooling energy demand. According to the set-up shownin Figure 11 the building model is connected with the CAO algorithm to investigate theautomatic controller design.

The control task was adjustment of the window opening factors given the constraints ofminimum energy demand and maximum level of comfort. That is, the CAO algorithmis given the authority to open or close the windows in a thermal zone, by means of aP-controller, deploying the state variables

• Outside Temperature (Tamb),

• Outside Relative Humidity (RHamb),

• Radiation on Window Facade (IT 0 90),

• Air Temperature of Zone R107 (Tair107), and

• Relative Humidity of Zone R107 (RH107).

Results for six virtual experiments are shown in the graphs of Figure 21. Each graph showsthe window opening factor for a prediction and control horizon of two days, the settlinginterval is not drawn. The range for variation was given by zero and the maximum valuesseen in the graphs (approximately 0.58).

4.2.1 Conditions

The most important boundary conditions for these VE were as follows:

• Simulation interval [4152-4200] hours, Mo 23rd and Tue 24th of June 2003

• Weather data from 2003 for the same time (see Figure 20)



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• Initial air temperature 23◦C (RH=50%)

• Natural ventilation is realized with TRNFLOW via window opening

Figure 20: Weather data for the conducted six virtual experiments.

4.2.2 Results Discussion

The results were obtained on different computers with slightly varying parameters for eachVE. The major difference is the maximum number of iterations for each VE. The appliedinitial controller (Θ0) would lead to closing of the window. After the following number ofiterations for the respective VE:

• VE 01: 1000 Iterations






window opening factors obtained are shown in Figure 21 on the vertical axis, and the timeis shown rotated on the horizontal axis. It refers to two subsequent days.



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Figure 21: Window opening factors for six virtual experiments with Night Ventilation

Night ventilation is clearly visible for VE 01 and VE 04. For all other VEs a tendency toclose the windows during the day is visible, but the edges are not abrupt and partly noisy.VE01 and VE 04 show desirable results.

The adaptive controllers and their individual gains leading to these opening factors aredrawn in Figure 22, and Figure 23 in a radar plot. Each polygon refers to one controller(five gains). The gains relating to the state variables are given by the intersection of thepolygon with the five axis. The normalization rule applied to the state variable is givenbelow the axis label in square brackets.

The gains for VE 01 and VE 04 are very similar, whether or not they stand for the samelocal optimum, is difficult so answer. VE02 and VE03, and VE05 and VE06 have verysimilar gains; the opening factors resulting from the respective controller are also verysimilar. Although a correlation between controller behavior and gains can be assumed, itis not certain that similar controller behavior originates from the same gains.



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Figure 22: Gains of two optimal controllers for Night Ventilation

Figure 23: Gains of four controllers for Night Ventilation



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4.3 Further VE on the TRNSYS simulator and expected energy savings

Results obtained for night ventilation are only of qualitative nature to demonstrate a rea-sonable behavior, automatically found by the controller optimization algorithm. Other VEwere conducted to initially test the CAO algorithm in connection with the TRNSYS simu-lator. Results were published at the BuildSys Conference 2011 in Seattle, and the paper isappended to this deliverable. In this paper a basic control task is defined (ideal cooling insummer) and tested in three zones of the tower. The main conclusion in terms of energysavings is 5% – 14% savings with respect to a simple rule based controller. The other spe-cial characteristic demonstrated by the found optimal controller, is a temporary discomfortallowed during times with occupancy.

Other tests offering interesting results with respect to energy savings were conducted fordeliverable D2.3. There the potential savings for proper shading control in winter over aweekend were analyzed. It showed the importance of an automatic control for this device,because closed shading prevents from passive solar energy utilization, especially for thermalzones with southwards (for the northern hemisphere) facing windows. In addition, thedesired prediction horizon was analyzed implicitly by these simulations. Ideally it is 72hours, however, the accuracy of weather forecast data is not considered in this first analysis.The specific energy savings on Monday, in case of optimal shading control over a sunnyweekend, were found to range between 42 Wh/m3 and 53 Wh/m3 in the office zones.



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5 New Building of the E.ON ERC, RWTH Aachen

5.1 Setting-up the Dymola/Modelica simulation

Figure 24 shows the model for one tower, as explained in section 2.3. The tower consistsof three identical offices, each situated on another floor, on top of each other. Adiabaticconditions are set at the walls towers neighboring offices and the corridor, as well as tothe room, under the office on the ground floor. Internal gains in the form of users are alsointegrated in the model. The model requires as inputs the schedule of the users, the heatflow from the ideal heater/coolers, the natural ventilation rate and the outside temperature,wind speed and solar radiation on the windows and the roof. The model outputs informationabout the free flowing air temperature in the room and the radiative temperature in theroom. These are used among others to calculate the Fanger PPD-index, which along withthe energy consumption of the technical equipment is used for evaluating the controllers.

Figure 24: Model of a tower with three offices

A model for calculating the Fanger PPD index was developed at EBC. The model requiresthe following inputs:

• the ambient temperature, for calculating the clothing resistance of the users

• the energy production of a person expressed in W/m2, evaluated at 70W/m2 for adesk job

• the energy done by outside machinery on the person, set at zero for such a desk job

• the free flowing air temperature in the room

• the radiative temperature in the room



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• the average air velocity, set at 0.1m/s. Accurate calculation of the air speed in theroom is not possible at this scale of the model and higher velocities over long periodsof time by rational ventilation are considered unlikely

• the pressure of water vapors in the air

Figure 25 shows the Fanger PPD characteristic generated by this model, for the variationof the free flowing air temperature, when the outside temperature is set at 10 ℃. Theoptimum lies at 23.37 ℃.

Figure 25: Results Fanger PPD for an outside temperature of 10 ℃

Figure 26 shows the simulation set-up in Dymola that is called from the BCVTB model.The simulation model consists of a tower model, a weather model, ideal heaters / coolers(with unlimited power) for each room, a shading device, blocks for computing the FangerPPD for each room and a communication block to BCVTB.

Figure 26: Simulation set-up in Dymola

The heating and cooling temperature set points of each heater / cooler and the degree of



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shading are given by the controller in MATLAB and build the seven (three heating, threecooling set points and the shade position) outputs of the BCVTB-block. As all officesare situated on side of the building, the shading is considered for all three offices, and notindependently for each office. Shading is interpreted as the factor of incident solar radiationon the window that is not allowed to pass into the room. Shading zero means, no shading,shading 1 means no solar radiation passes through the window.

The BCVTB block has nineteen inputs. Ten of them are used to calculate the temperatureset points and the degree of shading in MATLAB: the outside air temperature and humidity,the inside air temperature and humidity for each room and the solar radiation on the windowand on the roof. They are the so called “sensor measurements”. Six further inputs are usedto calculate the cost function: the energy consumption of the ideal heaters / coolers and thePPD value of the Fanger function for each room. The other three inputs give informationabout whether or not persons are present in the room at a certain point. When no personsare present in the room, for example outside office hours, the Fanger PPD values are set tozero in the “simulateAndExit.m” routine.

5.2 Adapting the MATLAB routines

The CAO Algorithm was provided by our colleagues at TUC. It contained a library forsupport vector machines, the main MATLAB function called “myAFT_SVM.m” and the“simulateAndExit.m” file. The last two files were provided from their models as they alsouse BCVTB to connect their building model in EnergyPlus to the CAO algorithm.

Several m-Files were provided, belonging to different simulations, for one or multiple zonebuildings. The files had to be adapted according to our own model:

• The controllers and the determining factors in calculating the set points were setaccordingly

• The sensor values were normalized once more

• The information about the Fanger PPD and the energy consumption were adaptedto the form required by the algorithm

5.3 Exemplary experiments

5.3.1 Cooling

As the building is very well isolated and has a large thermal mass, heating experiments arenot that interesting, since very little energy is needed in heating the building, especiallyduring office hours, because of the internal gains from the users and the machines.

This is why for this example a very hot day in summer was chosen. The day is the 6th ofJuly.



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Figure 27: Outside air temperature forthe 6th of July

Figure 28: Solar radiation on window forthe 6th of July

As shown in Figure 27, the outside air temperature during day goes up to 28.5 ℃. Anexperiment was done on an office in the first floor with a south-east orientation. The officehas a floor area of about 18m2 and a volume of about 50m3. The window area is 8m2.Only internal gains via users are modeled with two users being present in the room between7 and 16 o clock. The temperatures of the thermal loads in the walls are initialized with22 ℃. The HVAC system is considered an ideal cooler with unlimited power.

Two simulations are carried out, each time only controlling the cooling system, via thecooling temperature set point. One with a simple rule for HVAC that should keep thetemperature under 26 ℃, meaning as soon as the temperature goes over 26 ℃, the systemlowers it under 26 ℃. The other simulation is done with the controllers generated by theCAO algorithm. 100 iterations were used in determining the best controller. The upperlimit for the cooling set point temperature for the CAO algorithm was also set at 26 ℃,and the Fanger PPD constraint was set at 10%. This means that the mean value of theFanger PPD index during office hours should not exceed 10%.

The comparison between the two simulations is presented in the following figures. Theenergy consumption of the ideal cooler is 2.81 kWh for the simple rule, and 6.21 kWh withCAO. However as seen in Figure 29, the PPD values are significantly lower with the CAOgenerated controller, with a maximum in the afternoon of around 21%, whereas the simplerule simulation leads to a PPD value of 37%.



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Figure 29: Fanger PPD index values for the two simulations

These results are further explained by analyzing the free flowing air temperature in theroom and the cooling temperature set points. CAO chooses a temperature set point ofaround 24.5 ℃, allowing it to slightly go up near the end of the office hours. Because ofthe ideal cooler, the temperature in the room follows the set point quite faithfully.

Figure 30: Cooling temperature setpoints for the two simulations

Figure 31: Free flowing air temperaturefor the two simulations

Further simulations were done, using the simple rule controller, to try and achieve a resultclose enough to the one produced by the CAO algorithm. The closest one came by atemperature set point of 24.5 ℃.

5.4 Outlook

Work still needs to be done in setting up the PEBBLE system. The controllers have to bedeveloped using more developed models, which include the technical equipment availablein the office rooms. After this the controllers developed for each tower need to be tested



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through cross-simulation in order to decide how many different controllers are actuallyneeded.

A sensitivity analysis needs to be carried out to determine the adequate number of iterationsfor determining a set of controllers.

Year long simulations have to be carried out in order to analyze the energy saving possibil-ities of the PEBBLE system for a whole year.



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References

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index.xhtml.



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v.1.0, 30/08/2011

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A Rule-Based Controllers and Results

A.1 Cooling

Tab

le5:

Rul

e-ba

sed

cont

rolle

rvs

CA

Ofo

rJu

ne20

10in

Ath

ens

HVA

CFan

ger

PPD

Vio

lati

ons

Ener

gyPer

centa

geSet

poi

nts

Offi

ce4

Offi

ce5-

6O

ffice

11O

ffice

13O

ffice

1O

ffice

2O

ffice

3O

ffice

8O

ffice

9O

ffice

10C

onsu

mpti

onof

Tim

e(T

set

◦C

)Zon

e1

Zon

e1

Zon

e2

Zon

e2

Zon

e3

Zon

e3

Zon

e3

Zon

e4

Zon

e4

Zon

e4

(Kw

h)

Dis

sati

sfied

(%)

23◦C

6427

285

264

6714

416

310

818

531

412

26.6

16.5

24◦C

2461

1649

2131

1025

5627

1008

.33.

225

◦C

2544

1631

2327

723

4919

806.

62.

626

◦C

123

4058

3032

277

2348

1962

7.3

427

◦C

690

140

465

9444

715

523

353

171

1847

3.5

25.4

28◦C

857

336

712

333

785

612

330

704

601

249

346.

554

.829

◦C

857

376

720

358

813

635

360

718

624

275

250.

456

.930

◦C

857

384

721

367

815

642

367

726

630

285

181.

757

.5

CA

O25

5415

4924

257

2150

2062

5.9

2.8

CA

O_

RB

2442

1737

2326

621

4520

606.

22.

6



v.1.0, 30/08/2011

Final

Tab

le6:

Rul

e-ba

sed

cont

rolle

rvs

CA

Ofo

rth

ew

hole

sum

mer

of20

10in

Ath

ens

HVA

CFan

ger

PPD

Vio

lati

ons

Ener

gyTot

alSet

poi

nts

Offi

ce4

Offi

ce5-

6O

ffice

11O

ffice

13O

ffice

1O

ffice

2O

ffice

3O

ffice

8O

ffice

9O

ffice

10C

onsu

mpti

onV

iola

tion

(Tset

◦C

)Zon

e1

Zon

e1

Zon

e2

Zon

e2

Zon

e3

Zon

e3

Zon

e3

Zon

e4

Zon

e4

Zon

e4

(Kw

h)

Tim

e(h

)23

◦C

148

295

122

273

124

178

163

147

233

322

4064

.233

4.2

24◦C

118

6155

6095

7410

7511

327

3459

.511

2.7

25◦C

175

4459

4311

275

779

113

1928

85.2

118.

826

◦C

1263

113

621

108

150

857

9012

319

2365

.239

4.7

27◦C

2521

764

1991

721

1988

722

1670

860

171

1819

03.2

1777

.5

CA

O11

864

5392

113

7711

7911

037

2667

.712

5.7


Documents

Deliverable D3.3: Simulation Assessment of the PEBBLE ... · In this document simulation assessment studies for the three demonstration buildings are presented. The concept of co-simulation