Bio-Inspired Control Systems for Building Services to Save ... · Datalog Server EIB DDE Server Log file Matlab (controllers levels 2 and 3, calculations) Transfer file from VNR

Bio-Inspired Control Systems for Building Services to Save Energy

Consumption and Increase Indoor Comfort

Dr. Nicolas MorelSolar Energy & Building Physics Laboratory,

EPFL

October 2008

Bio-inspired control systems, Oct 2008 – Slide 2

É C O L E P O L Y T E C H N I Q U E FÉ DÉRALE D E LA USANNE

Buildings and Control Systems

Significant impact on the energy consumption–

decrease the energy consumption, while providing a similar prestation (i.e. user comfort: Tint, air quality, lighting level

and

quality, etc)

Common issues:–

solar gains are not taken correctly into account

–

artificial lighting is not controlled in a smart way–

no blind controller, or blind controller not well designed

–

cooling device operation not well controlled–

commissioning "forgotten"

–

no adaptation to user's preferences



Control systems: Research projects at LESO

DELTA (blind controller using Fuzzy Logic)Predictive Heating Stochastic Control (weather pred.)NEUROBAT (predictive heating control using ANN's)EDIFICIO (integrated room controller)Smart Window (integrated window controller)Sustainable Development LESO Building (EIB equip.)IEA SH&C Task 31AdControl (pred. control using GA's for user adaptation)Ecco-Build (solar shading control)BELControl (AdControl results commercial product)CCEM Control (Simulation and Comparison Tool)



Experimental Tools

LESO building, outside view (detail)



Experimental Tools

Inside view of an office room in the LESO building



Experimental Tools

Two windows in each room:–

lower window →

normal

window–

upper window →

anidolic

(non-imaging) daylighting system, window cannot be opened

Each window has its own blind (textile blind)

Ventingskylight

Plaster

12 cm mineral wool

Wood

Anidolicdaylighting

system



Experimental Tools

Data logging and EIB control bus in the LESO building

EIB Building Bus

Ethernet LESO Network

VNR-PC(Data Logger)

EIB-PC(EIB Control)

VNRmodule #1

VNRmodule #2

VNRmodule #3

Heatingcontrolroom1

Heatingcontrolroom2

Art. lightcontrolroom1

Blindcontrolroom1

Sensors & userroom1

Art. lightcontrolroom2

Blindcontrolroom2

Sensors & userroom2

sens

ors

Control-PC(control algorithms)



Experimental Tools

In each office room, following sensors and actuators are connected to the EIB bus:

Sensors:–

indoor air temperature

–

illuminance level–

user presence

–

window opening (switch)

Actuators:–

heating (on/off, pulse width modulation)

–

blind position (window & anidolic blinds)–

artificial lighting (on/off + continuous dimming)

User command buttons:–

setpoint temperature

–

blinds up/down–

artificial lighting (on/off + dimming)



Experimental Tools

UP 1141.3.10 UP 231

UP 1101.3.11

ECO IR 360

GE 2521.3.14

UP 2201.3.13

windowopeningcontacts

UP 2201.3.12

UP 5201.3.17

N 5621.3.18

GE 5251.3.15

lighting and temperature commandbuttons, room air temperature sensor

presence sensor

lightingsensor

lower window actuator

heating actuator

lightingactuator

EIB Room 201

blindcommandbuttons

UP 5201.3.16

anidolic window actuator

Reg 191/01

EIB

bus electric box



Experimental Tests Software

Data file

VNRDatalog Server

EIB DDEServer

Log file

Matlab(controllers

levels 2 and 3, calculations)

Transfer file

from VNRdata logger

to/from EIBinterface

Control-PC

through EthernetLESO network

EIB-PC

Java EIBServer

RMI (through Ethernet

LESO network)



Computer Tools: Bio-Inspiration

Bio-mimetic (or bio-inspired) building service controllers (for heating, cooling, ventilation, electric lighting, blinds, etc) take advantage of the analogy between a building and a living being: both are supposed to keep the inside climate rather constant, despite large variations of the environmental conditions.

"Soft computing" computing techniques are used to realize bio-mimetic control algorithms:–

Artificial Neural Networks

–

Fuzzy Logic–

Genetic Algorithms

Bio-mimetic control algorithms are bio-mimetic in two senses:–

they use computer techniques inspired from the Life Sciences

–

they allow a behaviour of the building analogous to a living being



Computer Tools: Predictive Controllers

For inertial building characteristics (e.g. thermal behaviour), the controller must take into account a realistic prevision of the boundary conditions



Computer Tools: Artificial Neural Networks

Artificial Neural Networks (ANN's) are able to modelize a building (e.g. here for its thermal behaviour) in an adaptive way



Computer Tools: Fuzzy Logic

Fuzzy Logic (FL) is a powerful way to represent domain knowledge (expertise), using an "human-like" language and processing (when talking about a person, we say "he's tall" and not "he's 188 cm tall").

The fuzzy variables are processed in a rule base gathering the expertise; the output is used as a control variable.

-10 0 10 20 30

0

0.2

0.4

0.6

0.8

1D

egre

e of

mem

bers

hip winter mid-season summer



Computer Tools: Genetic Algorithms

Genetic Algorithms (GA's) are used to build control systems that can "learn" from their environment or from the user's preferences (expressed through direct commands to the devices: blinds, etc).

The "genome" contains the parameters of controllers (fuzzy logic rules, physical models, etc).



Evolution of control systems

Manual systems

Automatic with simple algorithms

"Intelligent" ("smart") algorithms

"Intelligent" algorithms with adaptation to building characteristics and environmental conditions

"Intelligent" algorithms with adaptation to user’s behaviour



Why adaptivity is important

Adaptation to user’s wishes:–

not all users are the same

–

a control system going against the user is rejected

Adaptation to building characteristics and environmental conditions:–

commissioning a control system is a demanding activity and a condition for a good operation

–

nevertheless, commissioning is very often "forgotten"



Predictive Stochastic Heating Controller

Predictive heating controller using stochastic weather prediction

Algorithms used: stochastic model, cost function & dynamic programming

Partner (remote control application only):–

Costronic Inc, Lausanne



Predictive Stochastic Heating Controller: Principles

Weather conditions forecast variants:–

use the weather forecast provided by the specialised institutions (for example in Switzerland the Swiss Meteorology Institute)

–

stochastic prediction (probability of transition I(t1

) I(t2))–

prediction with an artificial neural network (or any other adaptive method)

Practical realization:–

predictive stochastic heating controller developed and experimented at LESO-PB/EPFL heating energy saving reaching 20 % (the saving is higher when the building and the heating system has a high thermal mass)

–

remote heating controller by Costronic SA



Predictive Stochastic Heating Controller: Results

floorf=0.3

airf=0.3

floorf=0.2

airf=0.2

Text + thermost.valves(reference)

8435 4440 7703 4086

thermostat on inside air 8054(-4.5 %)

4071(-8.3 %)

7395(-4.0 %)

3808(-6.9 %)

optimal stochasticcontrol

7143(-15.3 %)

4009(-9.7 %)

6926-10.1 %

3846(-5.9 %)

perfect prediction(fictive)

6622(- 21.5 %)

3680(-17.1 %)

6438(-16.4 %)

3658(-10.5 %)

Simulated heating consumption for one whole year [MJ]–

floor floor heating system, air convective air heating

–

f = window opening factor (Awin

/Afloor

)



NEUROBAT

Predictive and self-adaptive heating controller

Algorithms used: ANN, FL, cost function & dynamic programming

Partners:–

CSEM (Centre Suisse d'Electronique et de Microtechnique), Neuchâtel

–

Sauter, Basle–

Estia, Lausanne



NEUROBAT: Principles

Predictive controller similar to the Stochastic Heating Controller (same cost function)

Remedy a scamped or "forgotten" commissioning:–

the controller includes parameters which progressively adapt the

controller

to the real situation during the first operation weeks, thanks to the optimisation of a "cost function"

–

adaptation to the building characteristics–

adaptation to the user's behaviour

Cost function:–

J(t) = C1

· P + C2

·

f(discomfort)–

J([t1,t2]) = ∫

J(t) dt



NEUROBAT: Principles



NEUROBAT: Detailed Block Diagram

Heating power Adaptation to user

Mixing valvecontroller

Weather prediction

Optimal controllerBuilding model

User setpoints

Outside temperature,solar radiation

Room temperature

Inlet temperature

Popt UTint

P

-

Comforttemperature

Return temperature-

Kp

Building



NEUROBAT: Comfort Results

Inside temperature, °C (measurement, NEUROBAT vs. conventional):

15 20 25 300

0.2

0.4

0.6

0.8

1

15 20 25 300

0.2

0.4

0.6

0.8

1

PMV=Predicted Mean Vote (measurement, NEUROBAT vs. conventional):

-3 -2 -1 0 1 20

0.2

0.4

0.6

0.8

1

3 -3 -2 -1 0 1 20

0.2

0.4

0.6

0.8

1

3



NEUROBAT: Energy Results (Simulated)

46.7 46.7 46.7 46.7 46.7 47.7

104.2

83.0

75.8 75.4

67.2

49.0

0.0

20.0

40.0

60.0

80.0

100.0

120.0

Standardconventional

controller

Advancedconventional

controller

Performantconventional

controller

Very performantconventional

controller

NEUROBATcontroller

NEUROBATcontroller, withDELTA blind

controller

Ene

rgy

inde

x [M

J/m

2]

HeatElectricity



Project AdControl: Principles

Automatic control for building services (HVAC, blinds, electric lighting) leads to …

25% energy savings compared to standardcontrol systems (manual control)

… with a major drawback:

Users may be unsatisfied by the provided ambienceor possibly reject the automatic system

Proposed solution: a fuzzy logic controller for solar shading, electric lighting and heating with a continuous adaptation to user preferences, using the manual interactions



Structure of an adaptive control system

Adaptive models

Fuzzy logic rule base

Adaptation algorithms

Sensors Actuators

user's wishes

adjustable param

controlled variables

Exp

ert c

ontro

l sys

tem



Control optimization using GA's (1)

Expert control system for blinds, artificial lightingand heating devices

Userswishes

Fuzzy parameters adaptated through GAs:-Keep an energy efficient control-Learn and integrate the user preferences

http://cgl.microsoft.com/clipgallerylive/packages/j021/j0212709.cil

http://cgl.microsoft.com/clipgallerylive/packages/j021/j0215162.cil




How the GA's are applied for the adaptation of a controller ?

–

an individual is a given controller, each gene being one parameter of the fuzzy logic rule

–

fitness function = 1/[energy consumption + discomfort level]

–

for an adaptation to user's wishes, the discomfort level is supposed to be zero when the user expresses a wish




Aims of the adaptation:–

keeping an energy efficient control

→ CONTBASE, containing the current efficient controller–

adaptation and learning of the user preferences and behaviour

→ WISHBASE, containing all the wishes expressed by the user

Fitness function:

( ) ( ) ⎥⎦

⎤⎢⎣

⎡−⋅+−= ∑ ∑

j k

2kik

2jiji )wishbase()c(10)contbase()c(1)c(Fitness αααα



Project AdControl: Experimental tests

14 rooms– Completely equipped for

monitoring– Attributed randomly– « Single-blinded » study

23 users– Opinions assessed through questionnaires

9 months– 3 seasons x 3 types of control system



Project AdControl: Experimental results

Controller type

Energy saving

Thermal comfort (satisfaction)

Visual comfort (satisfaction)

Rejection after 4 weeks

Manual 0 % 84 % 86 % -

Smart, not adaptive

25 % 84 % 88 % 25 %

Smart, adaptive to user's preferences

24 % 86 % 89 % 5 %



Project AdControl: Conclusions

Adaptation to user's wishes is an essential feature of an advanced control system for building services.

If "intelligent" control systems allow a significant energy saving (up to 20 or 30 %), control systems adaptive to user's wishes allow to keep that number while increasing acceptance by users.

Experimental results (measurements with real users) confirm the interest of the concept developed, based on GA's.



Project AdControl: A Derived Commercial Product

Commercial product available from a small company of Winterthur (Adhoco, www.adhoco.com):

–

Based on the algorithms developed in the research project AdControl

–

Using wireless connexions for sensors and actuators

–

Central unit with low electric consumption

http://www.adhoco.com/



Central unit Weather station Blind actuator

Presence andlighting sensor

Temperature andhumidity sensor

Water radiatoractuator

Lamp switchingand dimming

Project AdControl: A Derived Commercial Product



Project Ecco-Build: Control Algorithm

Desidera for a solution:–

Extract maximum information from measured or modelled data

–

Do not use expensive sensors–

Learn from user behaviour

Bayesian classifier:

The controller aims at reducing the VDP (Visual Discomfort probability)

)Pr()Pr()Pr()Pr()Pr()Pr(

)Pr(

TrueCTrueCeEFalseCFalseCeEFalseCFalseCeE

eEFalseCVDP

===+======

====

Illuminance distribution for uncomfortable situations

Illuminance distribution for comfortable situations



Project Ecco-Build: Uncomfortable situations

)Pr( FalseCeE ==

0 500 1000 1500 2000 2500 3000 3500

0.00

00.

001

0.00

20.

003

0.00

4

[lux]

Den

sity

est

imat

e



Project Ecco-Build: Comfortable situations

0 500 1000 1500 2000 2500 3000 3500

0.00

000.

0005

0.00

100.

0015

[lux]

Den

sity

est

imat

e

)Pr( TrueCeE ==



Project Ecco-Build: Discomfort probability

)Pr( eEFalseC ==

0 500 1000 1500 2000 2500 3000 3500

0.0

0.2

0.4

0.6

0.8

1.0

[lux]

Dis

com

fort

pro

babi

lity



Project Ecco-Build: Controller operation

Visual comfort depends on illuminance on workplane, but also on other variables (luminances of surrounding surfaces, pupilar illuminance, etc) the illuminance on the other surfaces than workplane must be taken into accountThe controller operates solar shadings and electric lighting in such a way to reduce the visual discomfort, under the following conditions:–

Minimize the electricity consumption for the artificial lighting

system–

Minimize the risks of thermal discomfort (for instance caused by an overheating related with direct solar gains)

–

Maximize the solar gains when they are usuful (in winter)



Bio-Inspired Control Algorithms

Simultaneously:–

Reduce energy demand

–

Increase indoor comfort and acceptance by users

Buildings already equipped with sensors + actuators + building bus:–

Additional investments for a control system (control unit + software) are relatively small

Documents

Bio-Inspired Control Systems for Building Services to Save ... · Datalog Server EIB DDE Server Log file Matlab (controllers levels 2 and 3, calculations) Transfer file from VNR