Presenter: Bjarne W. Olesen | 2017-2018 ASHRAE President Rune Andersen Ph.D. Shaping Tomorrow’s Built Environment Today The influence of occupant behaviour on indoor environment and energy use in buildings October 12, 2017 Development towards Near Zero Energy Buildings • Not possible to reach goals through ‘traditional’ technologies – Envelope insulation – Building airtightness – Ventilation heat recovery • Other measures are needed – Demand controlled ventilation – Solar shading to control overheating and daylight – Lighting control – Window opening Robust technologies • No user interactions • Works with and without people in the building Sensitive technologies • User interactions required • Difficult to understand consequences The adaptive principle Adjust to the environment • clothing • activity level • posture • hot/cold drinks • etc. Adjust the environment • thermostat adjustments • window openings • electrical lights • solar shading • etc. The choice can affect energy use by up to 300% If a change occurs such as to produce discomfort, people react in ways which tend to restore their comfort To restore comfort occupants can
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Presenter: Bjarne W. Olesen | 2017-2018 ASHRAE PresidentRune Andersen Ph.D.
ShapingTomorrow’sBuiltEnvironmentToday
Theinfluence ofoccupant behaviour onindoor environment andenergy use inbuildingsOctober 12, 2017
DevelopmenttowardsNearZeroEnergyBuildings
• Notpossibletoreachgoalsthrough‘traditional’technologies– Envelopeinsulation– Buildingairtightness– Ventilationheatrecovery
• Othermeasuresareneeded– Demandcontrolled ventilation– Solarshading tocontrol
overheatinganddaylight– Lighting control– Window opening
Robusttechnologies• Nouserinteractions• Workswithandwithout
peopleinthebuilding
Sensitivetechnologies• Userinteractions
required• Difficulttounderstand
consequences
Theadaptiveprinciple
Adjustto theenvironment
• clothing• activitylevel• posture• hot/colddrinks• etc.
Adjusttheenvironment
• thermostatadjustments
• windowopenings• electricallights• solarshading• etc.
Thechoicecanaffectenergyusebyupto300%
Ifachangeoccurssuchastoproducediscomfort,peoplereactinwayswhichtendtorestoretheircomfort
Torestorecomfortoccupantscan
Typesofbehaviourwithimpactonindoorenvironmentand/orenergyuse
Adjustto theenvironment
• clothing• activitylevel• posture• hot/colddrinks• etc.
Adjusttheenvironment
• thermostatadjustments
• windowopenings• electricallights• solarshading• etc.
Activitieswithotheraimsthanadjusting(to)theenvironment
• cooking• electricalloads• Showering• etc.
Investigationofheatingusedin290identicalhouses*
• Correctionfordifferencesinouterwallarea– Endhousesvs.Middlehouses
• Highestusedupto20timeshigherthanlowest
• Stableusedistributionovertime
• Nomeasurementsofindoorenvironment 0
5
10
15
20
25
30
35
0 50 100 150 200n
um
ber
of
hou
ses
Heating used [kWh/(m² · year)]
1993
1994
1998
1999
Background
• OBhassignificantinfluenceonbuildingenergyuse
0123456789101112131415
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25����
������(kWh/m2 )
��� 2.3 kWh/m2 Average 2.3kWh/m2
Ener
gy C
onsu
mpt
ion
of C
oolin
g Sy
stem
(kW
h/m
2)
Apartment No.
0123456789101112131415
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25����
������(kWh/m2 )
��� 2.3 kWh/m2 Average 2.3kWh/m2
Ener
gy C
onsu
mpt
ion
of C
oolin
g Sy
stem
(kW
h/m
2)
Apartment No.
Elec
trici
ty C
onsu
mpt
ion
of C
oolin
g Sy
stem
0123456789101112131415
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25����
������(kWh/m2 )
��� 2.3 kWh/m2 Average 2.3kWh/m2
Ener
gy C
onsu
mpt
ion
of C
oolin
g Sy
stem
(kW
h/m
2)
Apartment No.
0123456789101112131415
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25����
������(kWh/m2 )
��� 2.3 kWh/m2 Average 2.3kWh/m2
Ener
gy C
onsu
mpt
ion
of C
oolin
g Sy
stem
(kW
h/m
2)
Apartment No.
Elec
trici
ty C
onsu
mpt
ion
of C
oolin
g Sy
stem
ThecoolingenergyconsumptiondataindifferentapartmentsofoneresidentialbuildinginBeijing,2006
Significant differences between different apartment units
Calculatedandmeasuredenergyusein135.311houses*
*Datafrom:SBI 2016:09,Forskellenmellemmåltogberegnetenergiforbrugtilopvarmningafparcelhuse
0
100
200
300
400
500
A B C D E F G
Energyuseinsinglefa
milyhou
ses[kW
h/m²]
Energycertificate
calculatedconsumption
measuredconsumption
NB:Nocorrection foruse ofwood burning stoves andfireplaces!
Sensitivitytochangesinoccupants’behaviour(robustness)*
• Simulationsofofficebuilding• Stochasticmodellingofoccupants’
– Windowopening– Solarshading
• Variationsto– windowarea– Thermalmass– Solarshading– Geographicallocation(climate)
RESULTS• Sensitive
– Lowthermalmass– largeglazingarea
• Robust– highmass– lowglazingarea
0%
5%
10%
15%
20%
25%
30%
35%
Relat
ive St
anda
rd De
viatio
n [%]
All zones ENERGY PERFORMANCE
ZONE 2 - WESTZONE 3a - SOUTHZONE 3b - S/WZONE 3c - S/EZONE 5 - EAST
shadings' variation in light envelope and more glazed models
shadings' variation in light envelope and less glazed models
shadings' variation in massive envelope and less glazed models
shadings' variation in massive envelope and more glazed models
*T.Busoetal.Occupantbehaviourandrobustnessofbuildingdesign.BuildingandEnvironment94(2015)694-703
Occupant Behaviour
• ECBAnnex 66DefinitionandSimulationofOccupantBehaviour inBuildings
• ASHRAEhasestablishedaMTG(MultipleTaskGroup)onOccupantBehaviour
• ObamaissuesanExecutiveOrder(EO)– BehavioralScienceInsightsPolicyDirective
Sharedorindividualheatingcost*
Collectivebilling Individualbilling
Measurementsin39apartments
Measurementsin17apartments
*S.Andersenetal./BuildingandEnvironment101(2016)1-8
12 DTU Civil Engineering, Technical University of Denmark
Heating season 2013-2014*
Interviews:• Individual billing
– focus on heat savings – Accepted uncomfortable
conditions to save money• Collective billing
– Focus on health, comfort and avoiding moisture problems
• Feedback created increased awareness of indoor environment
– 5 residents found feedback useful
– Daily routines changed in 2 apartments as result of feedback
0
25
50
75
100
400 600 800 1000 1200 1400
Prop
ortio
n of
mea
suring
pe
riod
[%
]
CO₂ cncentration [ppm]
Individual
Collective
Average
0
25
50
75
100
15 16 17 18 19 20 21 22 23 24 25 26 27Prop
ortio
n of
mea
suring
pe
riod
[%
]
Temperature [�C]
individualcollectiveaverage
*S. Andersen et al. Influence of heat cost allocation on occupants’ control of indoor environment in 56 apartments: studied with measurements, interviews and questionnaires .Building and Environment 101 (2016) 1-8
Howmanyofyouhavecheckedyour…
• emailwithinthelasthour?• Facebook/twitteraccounttoday?• bankaccountthisweek?• energymeterthismonth?• energymeterthisyear?
– Doyourememberhowmuchyoupay?
Feedback
• Letterseachweekto14apartmentsincluding– Overallevaluationofindoorenvironment
– Personaltips– Focusoftheweek– Comparisonwithaverage
• 3apartmentsactedasreference
CO2 Concentrationbeforeandduringfeedback
Beforefeedback:7.Septembertil8.November2015Feedback:9.Novemberto28.November2015
Behaviour– randomorpatterns?
• Largedifferencesbetweenapartments
– Noneoftheapartmentscovertheentiretemperaturerange
• Energyconsumptionfollowsresidents,whentheymove
• Patternscanbemodelled– andsimulated
0
20
40
60
80
100
15 16 17 18 19 20 21 22 23 24 25 26 27
Percen
tageoftim
e[%
]
Temperature[°C]
Measurements typicalset-pointinsimulations
C tv C TH B tv B th A tv A th
561 45 32 57 40 55
439 35 30 13 27 52
376 70 19 48 36 15
278 63 34 65 25 40
145 43 5 28 6 88
Ventilationinschoolswithorwithoutfeedback
Winter-heatingseason,windowsopeningandCO2
Wargockietal.,2014
Preeminent drivers
View outsideSky condition
Visual access
Visual exposureWorkstation position
Glare
LuminancecontrastIncident angle of
the sun
Light control position
Preference in natural light
Outdoor TemperatureIndoor Temperature
Dwelling typeWind speed
User’s age
User’s indoor temperature preferences
Solar radiation
Room type
User’s presence
Wind direction
Rain direction
Vapour production
Time of dayOrientation ofthe window
HouseholdsactivitiesPreviouswindow state
Season
Temperature control type
Ownership
ThermalbackgroundBehaviouralbackground
User’s gender
Annualincome
IAQ
Noise
A complex issue – Drivers reported in literature*
*from Fabi et al. ClimaMed 2011
Modelsofoccupants’windowopeningbehaviour
• Manymodels• Most,onlyrelyonthermalenvironment– Isthatenough?– WhichoneshouldIuse?
• Lackofvalidation• Lackofvalidationmethods
0
0,2
0,4
0,6
0,8
1
-10 -5 0 5 10 15 20 25 30 35
Pro
bab
ility
of
win
dow
op
en
Outdoor Temperature [�C]Andersen et al. 2009Andersen et al. 2012Haldi and Robinson 2008Nicol and Humphreys 2004 - EuropeNicol and Humphreys 2004 - PakistanRijal et al. 2007 - UK LongitudinalRijal et al. 2007 - UK Transverse
21 DTU Civil Engineering, Technical University of Denmark
Measurements in 16 dwellings*•Behaviour
–Window opening–Heating set-point
•Indoor environment–Temperature–Relative humidity–CO2 concentration
•Weather–Temperature–Wind speed–Solar radiation–Relative humidity
*R. Andersen et al. Building and Environment 2013
22 DTU Civil Engineering, Technical University of Denmark
Models of occupant behaviour*
0
0,02
0,04
0,06
0,08
0,1
0,12
0,14
300 800 1300 1800 2300
Pro
bab
ility
of
open
ing
[-]
CO₂ concentration [ppm]
Night Morning Day
Afternoon Evening
*R. Andersen et al. Building and Environment 2013
S. D’oca et al. HAVC&R 2013
0
0,02
0,04
0,06
0,08
0,1
-10 0 10 20 30
Pro
bab
ility
of
incr
easi
ng
set
-poi
nt
[-]
Outdoor temperature [�C]
Night Morning Day
Afternoon Evening
23 DTU Civil Engineering, Technical University of Denmark
How is behaviour modelled?
Thermal comfort
•Temperature > 25 �C →Window open
•Temperature < 21 �C →Window closed
•Heating set-point = 20 �C
Air quality
•Constant ventilation rate
Fixed schedule
•Windows open from 8.00 to 8.15 every day
Examples from praxis•Often based on designers own observations
–representative? –Comparable?
Tendency to view occupants as rational controllers of IEQ
and energy
Fromdeterministictostochasticmodelling
Deterministicmodelofphysicalaspects
Windowopening
Heatingset-points
Coolingset-points
lighting
…
Stochasticmodels
Probabilitydistributionofperformanceindicators
0
0,1
0,2
0,3
0,4
0,5
0 5 10 15
0%
50%
100%
-1000 1000 3000 5000
pro
bab
ility
of
op
enin
g
CO2 concentration
Measurements in 16 dwellings*•Behaviour
–Window opening–Heating set-point
•Indoor environment–Temperature–Relative humidity–CO2 concentration
•Weather–Temperature–Wind speed–Solar radiation–Relative humidity
*R. Andersen et al. Building and Environment 2013
Verificationofbehaviourmodels• Measurementsinfive
apartments– temperature– CO2concentration– Relativehumidity
• Simulationsbasedonquestionnaireandobservations
• Stochasticmodels– Windowopening– Thermostatadjustments– Inferredfromdetailed
measurementsinapartments
Measurementsvs.simulation-overall*
*R.Andersenetal.EnergyandBuildings2016
• Stochasticsimulations– temperaturerangesratherthanfixedfigures
• Simulatedtemperaturesinthesamerangeasmeasurements
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
14 16 18 20 22 24 26 28 30 32
Percen
tageoftim
e
Indoortemperatureinbedroom[°C]
Greyarea:simulationresults
Black curves: measurements
Measurementsvs.simulation– ineachtimestep*
y=0,60x+7,57R²=0,21
y=x
10
15
20
25
30
35
15 17 19 21 23 25 27 29
Simulated
indo
orte
mpe
rature[°C]
Measuredindoortemperature[°C]
*R.Andersenetal.EnergyandBuildings2016
29 DTU Civil Engineering, Technical University of Denmark
Effect of occupants’ behaviour on energy consumption
•Differences in occupants’ behaviour leads to–large differences in some buildings
–smaller differences in other buildings
•Why?
05
101520253035
0 100 200nu
mb
er o
f h
ouse
s
Heating consumption [kWh/(m² · year)]
1993
1994
1998
1999
Figure from: Andersen R, Proceedings of Healthy Buildings 2012, Brisbane, Australia
0
0,01
0,02
0,03
0,04
0,05
0 50 100 150 200
pro
bab
ility
energy consumption
sensitiverobust
30 DTU Civil Engineering, Technical University of Denmark
Sensitivity to changes in occupants’ behaviour (robustness)*
• Simulations of office building• Stochastic modelling of occupants’
– Window opening– Solar shading
• Variations to – window area– Thermal mass– Solar shading– Geographical location (climate)
RESULTS• Sensitive
– Low thermal mass – large glazing area
• Robust– high mass – low glazing area 0%
5%
10%
15%
20%
25%
30%
35%
Relat
ive St
anda
rd De
viatio
n [%]
All zones ENERGY PERFORMANCE
ZONE 2 - WESTZONE 3a - SOUTHZONE 3b - S/WZONE 3c - S/EZONE 5 - EAST
shadings' variation in light envelope and more glazed models
shadings' variation in light envelope and less glazed models
shadings' variation in massive envelope and less glazed models
shadings' variation in massive envelope and more glazed models
* T. Buso et al. Occupant behaviour and robustness of building design. Building and Environment 94 (2015) 694-703
Summary
•In simulations, behaviour is just as important as weather and physical properties of the building
•Models of occupants’ behaviour implemented in existing simulation software
•We need more qualitative studies to investigate drivers of behaviour
•We need large datasets including all major driving variables
•We need good validation methods
–And data to validate models
•This work is performed in IEA-EBC annex 66
Related Documents
