Influence of shading control patterns on the energy assessment of office spaces

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Energy

and

Buildings

jo u r n al h om epa g e :w w w . e l s e v i e r . c o m / l o c a t e / e n b u i l d

Influence

of

shading

control

patterns

on

the

energy

assessment

of

office

spaces

Pedro

Correia

da

Silva

a,∗

, Vítor

Leal

a

,

Marilyne

Andersen

b

aMechanicalEngineeringDepartment,FacultyofEngineeringoftheUniversityofPorto(FEUP),RuaDr.RobertoFrias,s.n.,4200-465Porto,Portugal

bInterdisciplinaryLaboratoryofPerformance-IntegratedDesign(LIPID),SchoolofArchitecture,CivilandEnvironmentalEngineering(ENAC),ÉcolePolytechniqueFédéraledeLausanne

(EPFL),Switzerland

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received9November2011

Receivedinrevisedform24February2012 Accepted2March2012 Keywords: Visualcomfort Energyefficiency Lighting Glare Buildingsimulation Behavioralmodel

a

b

s

t

r

a

c

t

This workreviews existingmodelsof controlpatterns foroccupant–shadinginteractions inoffice buildings,andstudiestheirinfluenceintermsofenergydemandwhencomparingtransparentfac¸ade alternatives.Itstartsbyestablishingareviewofvisualcomfortcriteriainofficebuildingsandofthe conditionsthatpromptoccupantstointeractwithshadingdevicesandelectriclighting.Giventhelarge varietyofparametersidentifiedasprimaryvariablesintheexistingliterature–hencethevarietyof conditionsconsideredcomfortabledependingonthechosenreference– asensitivitystudywascarried outbasedondynamicsimulations.Theaimofthestudywastocharacterizetheimpactofchoosinga givenshadingcontrolmodel(patternorstrategy)onthecalculatedoverallenergydemandforheating, coolingandlighting,aswellastheimpactonchoosingthebest-performingtransparentfac¸adeoption forasingle-occupantoffice.Theresultsshowthatboththecalculatedenergyperformanceandthe rank-ingoftransparentfac¸adealternatives(glazingandshading)oftenvaryverysignificantlywithcontrol patternsconsideredfortheoccupant-shadinginteraction.Theyfurthershowthat,amongsttheeleven controlstrategiesthatwereconsidered,thebehavioralmodelbasedonaglareacceptabilitythreshold (expressedasDGI>20)istheonethat,whenconsideredindividually,wouldmostreliablyexpressan averagerankingfromallconsideredstrategies.Theimplicationsofthesefindingsarediscussedinview oftheirapplicabilitytoenergyperformance-basedfac¸adedesignchoicesevaluationaswellastofac¸ade designchoices.

©2012ElsevierB.V.Allrightsreserved.

1. Introduction

Glazedsurfaceshaveanimpactontheenergydemandfor light-ing, heating and coolingof buildings. Combined, theytypically accountformorethan50%oftheoverallenergydemandofoffice buildingsinOECDcountriesandoftenevenasmuchasover70% [1].Theadoptionofdesignmethodstoselectglazingandshading devicesshouldleadtothechoiceofsolutionsthatensureagood levelofenergyefficiency,whileguaranteeingestheticalqualityand visualcomforttobuildingoccupants.

Itiscommonknowledgethatbuildingoccupantstendtoadjust the lighting and shading devices dynamically, as a function of theindoorandoutdoorenvironmentalconditionsandoftheway theseconditionsmightimpacttheirvisualcomfortperception,in additiontootherbehavioralmotivationsindependentoftheir envi-ronment.Thedriversforthisbehaviorhavetypicallybeenreported intheformsofworkplaneilluminance[2–5],luminance[6,7],glare

∗ Correspondingauthor.Tel.:+351225081400. E-mailaddress:pedro.correia.silva@fe.up.pt(P.C.daSilva).

indexes[8,9],solarradiation[6,8,10–13]and/oroccupationperiod [12,14].

Althoughoccupantbehavioristhereforetheresultofmultiple factorsandcriteria,themostcommonlyusedmethodstoevaluate heatingandcoolingannualdemandtypicallyassumethat shad-ingdevicesremaininafixedpositionduringheatingandcooling season.Thisassumptionhasinfactbeenadoptedbythebuilding energyregulationsofmanycountries,suchastheRSECEinPortugal [15,16]:itassumesthatmovableshadings arenever active dur-ingtheheatingseason,andthat70%oftheglazingareaisshaded duringthecoolingseason.Ontheotherhand,theENISO13790 [17]recommendsamoredynamicmethod,basedonthe assump-tionthattheshadingdevicesareusedwhenevertheintensityof thesolarirradiationonthewindowexceeds300W/m2. Regard-ing electriclighting, the typicalassumption interms of energy consumptionevaluationisthatitreliesonuser-definedandfixed daily/weekly/seasonalschedules[18].Assuch,itcan,again,be con-sideredanon-dynamicapproach.

Parameters for deterministiccontrol patterns (static thresh-olds), includingworkplane illuminance,glareindexes and solar radiation, are already integrated in simulation frameworks like

0378-7788/$–seefrontmatter©2012ElsevierB.V.Allrightsreserved. doi:10.1016/j.enbuild.2012.03.019

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Listofsymbolsandabbreviations

VDT videodisplayterminal

EWorkplaneDaylight workplaneaverageilluminanceduesolely todaylight(lux)

EMax maximumilluminanceofthetaskarea(lux) EMin minimumilluminanceofthetaskarea(lux) ETask averageilluminanceofthetaskarea(lux)

ESurround illuminance of the immediate surrounding task area(bandwithawidthofatleast0.5m)(lux) LWindow averagewindowluminance(cd/m2)

LVisualfield luminancedistributionofthefieldofview(cd/m2) LSurround luminanceoftheimmediatesurroundingtaskarea

(bandwithawidthofatleast0.5m)(cd/m2) LPaper paperluminance(cd/m2)

LSurround- tasksurroundingsurfacesluminance(–) LVDT VDTluminance(cd/m2)

U-value coefficientofheattransmission(W/(m2◦C) g-Value totalsolartransmittancethroughwindows(–) TSol solartransmittancethroughwindows(–) TVis lighttransmittancethroughwindows(–)

EnergyPlus [19], ESP-r[20] or TRNSYS[21].The main assump-tion there is that shading devices or electric lighting are activated whenever the control condition is verified. More sophisticatedmethods,suchasthestochasticmodel Lightswitch-2002 [22] for example, have also been implemented in one dynamic energy simulation platform [23]. This comprehensive modelintegratesdifferentprobabilisticcriteria accordingtothe dynamic of the space occupation (arrival, intermediate or at departure).

Giventhevarietyofcontrolparametersandbehavioral mod-els,itisofoutmostimportancetoassesshowthecontrolpatterns forshadingdevicesandelectriclightingmightaffecttheresulting energyperformanceand,consequently,towhatextenttheymight alsoaffectwhichalternativemightbechosenintermsofglazing areaandtype,orshadingdevice.Thisstudythusaimstoassessthe impactofconsideringagivencontrolmodelinsteadofanotheron predictedenergyconsumption,andtoevaluatehowconsistentthe differentbehavioralmodelscurrentlyusedindynamicsimulation areintermsofoutcomesandreportedcontrolcriteria.Towardthis end,thepaperstartswithaliteraturereviewofthevisual com-fortcriteria(Section2)and oftheinteractionpatternsbetween buildingoccupantsandlightingorshadingdevices(Section3).To evaluatetheimpactofchoosingagivenshadingcontrolmodelon determiningthebest-performingtransparentfac¸adeoptionfora single-occupantoffice,asensitivitystudywasconducted,described inSection4andbasedonEnergyPlussimulationsofoverallenergy consumptionforheating,coolingandlighting.Theresultsofthis sensitivitystudyarepresentedinSection5andfurtheranalyzed anddiscussedinSection6.

2. Visualcomfortcriteria

There are numerous parameters in human psychology that influence the perception of lighting quality. Such parameters include, amongst others, mood, access to a direct view tothe outdoors and the occupants’ need for privacy, and cannot be objectivelymeasuredSomeauthorsarguethat,besidesenabling visualcomfort,“lightingquality”requirementsshouldalso com-priseproperconditionsfortaskperformanceandenergy-efficiency considerations[24].Therealsoseemstobeageneralagreement thatthemostbasicvisualcomfort requirementsrelatetolevels ofilluminanceandtohowlightisdistributedinthevisualfield [24–31].Thischapterpresentsabibliographicreviewofthemain physicallymeasurableparametersthatdrivevisualcomfort,based onilluminanceandluminancedistributions.

2.1. Illuminance

Illuminanceisthequantityunderlyingvisualcomfort require-ments that is the most often referred to in lighting literature. Table1summarizestherecommendationsfoundfromdifferent reviewedsources,regardingboth absoluteilluminanceand illu-minanceratios(contrast).Thetableshowsthatforofficebuildings, therecommendationsfortheminimumhorizontalilluminanceat theworkplanevaryfrom200to600luxfortypicalwriting, typ-ingandreadingofficetasks.Forcomputerbasedtasks,however, therecommendationsrangeisbetween100and300lux, signifi-cantlylowerthanforpaper-basedwork.Thetablealsopresents recommendationsformaximumilluminancelevelsonthe work-planerangingbetween1280and1800lux,implying thatabove those levelsglareis likely tooccur. In addition, tableindicates recommendationsfortheratiobetweenminimumandmaximum workplaneilluminance,whichshouldbekepthigherthan0.7,and ratiosbetweenhorizontalilluminanceofthetaskimmediate sur-roundingareasandilluminanceofthetaskbetween0.2and0.8 meaningthatilluminanceofthetaskshallbehigherthanthetask surroundings.

The type of tasks (computer-basedor paper-based)and the sourceofrecommendation(8differentones)leadtovariationsfor referenceworkplaneilluminanceofafactorof3.Consequentlythe consideredreferenceforbuildingdesignandoperationwill signif-icantlyimpactthebuildingenergyconsumption–directlyforthe electriclightingandindirectlyforthespaceheatingandcooling. 2.2. Luminancedistribution

Theluminancedistributionaffectstaskvisibility,comfortand perceptionofbrightnessofaspace[27].Table2presentsa sum-maryoftheliteraturereviewaddressingabsoluteluminancesand luminance ratios. Recommendations that consider the average luminanceofthevisualfield,wallluminance,ceilingluminance andworkplaneluminancewereallfoundwithhighlyvarying rec-ommendedranges.According totheserecommendations, walls andworkplanewouldhavetohavesignificantlylowerluminances thantheceiling.Therecommendationsforluminanceratiostake

Table1

Illuminancerecommendationsforoffices.

Source Parameter Recommendationsrangeforcomfortable

spaces

[25–27,32–37] Workplaneilluminance(lux) [100–300]–computerbasedtasks; [200–600]–paperbasedtasks; 1280–1800–maximumvalues

[25,34] EMin/EMaxWorkplane >0.5–accepted

>0.7– recommended

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Table2

Luminancelevelsrecommendationsforoffices.

Source Parameter Recommendationsrangeforcomfortablespaces

[30,38] Visualfieldaverageluminance(cd/m2) [20–75]

[25,27,29,30] Wallluminance(cd/m2) [5–179]maximumvalue:1000

[25,27,35] Ceilingluminance(cd/m2) [425–850]maximumvalue:1000

[29,30] Workplaneluminance(cd/m2) [40–105]

[39,40] Glaresourceluminance(cd/m2) Maximumvalue2500

[41,42] Windowluminance(cd/m2) Maximumvalues:[4000–6000]

[25,27,29,30,35] LPaper/Lurround [0.33–3]

[25,27] LVDT/Lurround [0.33–3]

intoconsiderationtheratiosofpaperorVDTluminanceand sur-roundingarealuminance,withrecommendedratiosofatmost1:3 betweenthetaskandtheimmediatesurroundingarealuminance. Abibliographicreviewregardingthecompatibilityof recom-mendationsreferring toluminancefoundnoresults. Significant discrepanciesmightbeexpectedregardingthedesignoptionsand theenergyconsumptionthatwouldresultfromtheadoptionof differentrecommendationsofluminancelevels.

Oneoftheundesiredeffectsofinadequateluminance distribu-tionisglare,avisualphenomenonthatcanbecausedbytoomuch brightnessortoohighluminanceratiosinthefieldofvision. Sev-eralindicesandmetrichavebeeninvestigatedtotrytocharacterize thetendencyofgivenindoorconditionstocausethissensation. TheVisualComfortProbability(VCP)[43],predictsthepercentage ofpopulationthatwillacceptagivenlightingconditionas com-fortablebasedonelectriclighting.Analternativewasdeveloped bytheInternationalCommissiononillumination(CIE)toproduce aconsensusglarecalculationsystemandnamedtheUnifiedGlare Rating(UGR).ItsmainenhancementcomparedtoVCPwasthe sim-plercalculationprocedure,whichmadeitultimatelysubstitutethe VCP[44].

For daylighting, which involveswide luminance ranges and potentialglaresources,severalparameterscanbefound.Oneof thesimplestisthemaximumwindowluminance,asreportedby Platzer[41]andalsoadoptedbyEN14501[42].Somespecific day-lightglareindiceswereandarestillbeingdeveloped.Thedaylight glareindex(DGI),commonlyreferredtointhecalculationof day-lightdiscomfortglare[45],isbasedonamathematicalformulation thatusestheglaresourceluminance(averagewindowluminance), thesolidangle,thebackgroundluminance(averageluminanceof thefieldofview,excludingglaresource)andthepositionindex. Itindicatesthedegreeofdiscomfortglareduetodaylight.Some limitationsinitsapplicationwereidentifiedforthepredictionof glareunderrealskyconditionsandforsituationswheretheglare sourceis non-uniformor fillsapproximatelythewholefield of view[39,44,46].AsaresultNazzal[47,48]proposedchangestothe calculationoftheDGI,introducingthenewdaylightglareindex (DGIN),whosemainenhancementconsistedofincreasedaccuracy relativelyto theDGIformulation. Its adequacy topredict glare

sensationhasnotyetbeenvalidatedwithotherindependent stud-ies.Anotherrecent indexcharacterizing discomfortglareis the daylightglareprobability(DGP)[49].Itintendstoexpressthe per-centageofpeopleexperiencingglareinagivenvisualconditionand isbasedontheverticaleyeilluminance,theglaresources lumi-nance,thesolidangleand thepositionindex.Itwasdeveloped consideringseveraldaylightingconditions,analyzingtheresponse of70subjectsintwodifferentlocations,Copenhagen(Denmark) andFreiburg (Germany).Theevaluationofexperimentalresults andusersresponsehasshownagoodcorrelationbetweenDGPand discomfortglareassubjectivelyassessedbyoccupants,betterthan anyofthepreviouslymentionedindices[49].Anotable develop-mentisDGP’sstrongdependencyonverticaleyeilluminance,much strongerthanothertestedfunctionslikewindowluminance,DGI andtheCIEglareindex[9].Theconsiderationofthevertical illumi-nanceasameasureoftheeyeadaptationlevelrepresentsthemain differenceofDGPrelativelytoUGR,DGIandDGIN,whichconsider thebackgroundluminanceasadaptationparameter,overcoming thedifficultyofthoseindexestopredictglarefromlargesources.

Table3summarizesthemainparametersfoundtocharacterize glareandtherespectiverecommendedrangesforvisualcomfort. WhileVCPandUGRweredevelopedforevaluatingthelevelof com-fortofindoorenvironmentslitbyartificiallighting,DGIandDGP weredevelopedtoexpressthevisualcomfortunderdaylight con-ditions.Theconsiderationofglareindexesduringbuildingdesign and/oroperationdirectlyinfluencethechoiceofthelamps,daylight systemsorindoorenvironmentfeaturesandtheresultingoverall energyconsumption.

2.3. Otherparametersrelatedtolightingquality

Beyondilluminanceandluminancedistributionconsiderations, certainlightingqualitiesthatcontributetoaspaceoveralllighting performanceintermsofoccupantssatisfactionmightalsoinfluence visualcomforteventhoughtheyarenotaseasytomeasure objec-tively.Thesewouldtypicallyincludelightdirectionality,itsspectral distributionandaccesstoadirectview.Someauthors[50–52]also arguethatsincelight“affectstheappearanceofthree-dimensional objects” [51],thepotential oflighting toproduceshadows and

Table3

Mainparameterstocharacterizeglareandtherespectiverecommendedrangesforvisualcomfort.

Source Parameter Recommendationsrangeforcomfortable

spaces

[27] Visualcomfortprobability(VCP) >70–recommendedvalue;

>80–minimizingdiscomfortglare;

[34,35,39] Unifiedglarerating(UGR) 19–maximum(offices);

>22–uncomfortable;

[39] Daylightglareindex(DGI) ≤22–comfortable;

24–26–uncomfortable ≥28–intolerable

[9,49] Daylightglareprobability(DGP) (DGPlimitvalue–95%of office-timeweakerthanthe perceivedglaresensation)

≤0.35–imperceptible(bestclass–A) ≤0.40–perceptible(goodclass–B) ≤0.45– disturbing(reasonableclass–C) >0.45–intolerable

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highlightpatternsinathree-dimensionalspaceshouldbe acknowl-edgedasanimportantaspectoflightdistributionassessment.

Intermsofcolorandappearance,itiswellknownthatwhile daylight’sspectrumguaranteesexcellentcolorrendering[53],the useofsomechemicalcoatingsonglazingswithlow-emissivityor solarprotectionperformancewillbothreducetheamountof trans-mitteddaylightandmodifyitsspectraldistribution.Aliterature reviewpresentedbyDubois[54]refersthat,ingeneral,the spec-tralpropertiesofa glazingdirectlyinfluencestheacceptance of theresultingindoorenvironment,seemingthatoccupantstendto preferthedaylightresultingfrombronzeglazingoverthedaylight resultingfromneutralandblueglazings[55]

Finally,accesstoadirectviewoftheoutdoorenvironmenthas beenreportedasanimportantcontributortothevisualcomfort perceptioninaroom[42,56,57].Occupantsalsoseemtoprefera naturalratherthanabuiltorurbanview[58].Recently,further studiesandfieldworkhavebeenconductedtobetterunderstand theroleoftheviewout(withvaryingdegreesof‘interest’)in a space’svisualperceptionandpotentialforvisualdiscomfort[59,57] andtendtoreportapositiveinfluenceoftheviewoutonperceived visualdiscomfort.Othertangiblevariablesthatarealsoreferredto intheliteratureasinfluentialinvisualcomfortperceptioninclude theflickerrateofartificiallightsources,thetypeoflightingsystem (directversusindirect)andthetypeoflightingcontrol[27,30].

Thereisalargerangeofcriteriathatareindicatedinthe litera-turethataffirmtoensurevisualcomfortorrepresentingoccupants preferencesinoffice buildings.Significantdifferencesarefound among the criteria, which makes also expect considerable dif-ferencesinsubsequentchoices ofbuildingdesign optionsor in evaluatingbuildingenergyconsumptionperformance.

3. Conditionsthatcauseofmanualoperationofshading

devicesandelectriclighting

Severalreasonshavebeenindicatedaspotentiallymotivating factorsin thecontrol of shadingdevices or electric lighting by occupantsofofficebuildings.Thissectionsummarizesthe condi-tionsthatseemtocauseamanualoperationofshadingdevicesand electriclightingasindicatedintheliterature.

Tables4and5identifythecriteriathatareusedtomodel occu-pantsoperationofshadingdevices (Table4)orelectric lighting (Table5).Theliteraturereview revealedthat suchcriteriawere usuallybasedonphysicalparametersthatwereeithermeasurable orcomputable,andinvolvingastrongconnectionbetweenlighting qualityandthestimulustoadjustshadingorelectriclighting.Some ofthecriteriastraightforwardlyuseparametersdescribedin Sec-tion2basedonilluminanceand/orluminancedistributions,while othersrelatetoweatherconditions,basedonsolarradiationand externalilluminance.

Theoccupants’behaviorpatternswereidentifiedfrombothfield monitoringin buildingsand laboratory data.However,most of thereportedcriteriawerebasedonalimitednumber ofoffices inspecificlocationsandinrestrictedmonitoringperiods.Theuse ofsuchcriteriathereforerequirestheexactassessmentconditions tobetakenintoaccount,makinggeneralizationdifficult.Mostof thecriteriaindicatedinTables4and5arestatictriggeringvalues, withfixedfrontiervaluesthatcharacterizeadeterministicmodel. Adifferentapproachhasalsobeentriedbysomeauthorswhere occupants’actionsare insteadcharacterizedbya probability of occurrence[2,3,6,14].

AccordingtoTable4,officebuildingoccupantswillactivateor deactivatetheshadingsbasedonthreedifferenttypesofcriteria:

(i)quantityofdaylight(illuminance)thatfallsontheworkplane;

(ii)visualdiscomfortrelatedtoglare,accountedindirectlyby win-dow luminances, transmittedsolar radiation or directlyby daylightglareindexes;

(iii)directsolarradiation,whichcancreateboththermalandvisual discomfort.

Thereisnoagreementaboutwhichbehavioralmodelorcontrol patternbestpredictsoccupantsactionsforeachbuildingdesign andweatherconditions,Furthermore,manyareformulatedin dif-ferentprimaryparameters,whichmakesitdifficulttoassesstheir compatibility.Nevertheless,thisreviewsuggeststhefollowing: • Solarradiationhasbeenthemostcitedparameterdrivingshading

control.

• Amongthedifferentsources,solarradiationisexpressedin dif-ferentways,suchasdirectorglobalsolarradiation,incidentor transmittedsolarradiation,beamorverticalsolarradiation.It isverydifficulttoassessthecompatibilityofthedifferent crite-ria,especiallybecauseeachcriteriawasdevelopedunderspecific researchconditions.

• Thecriteriabasedonvisualdiscomforthavefewcitations, prob-ablydenotingthatthemethodstoassessglareconditionsare insufficientlyknownortrustedbytheacademiccommunitynot specifictovisualcomfortstudies.

• Activatingtheshadingdevicesismoredependentofthe environ-mentalconditionsthandeactivatingtheshadings.Thenumberof studiesreferringthecriteriafordeactivatingshadingdevicesis verylow.

• Themajorityofcriteriaindicatedeterministicthreshold param-eters; however the more recent studies report probabilistic correlationsfortheshadingpositionsadjustments.

Asfarastheinteractionbetweenbuildingoccupantsand elec-triclightingisconcerned,ithasbeentheobjectofstudyfordecades withsome widely acceptedpatterns [3,61]. Table 5 presents a review of thecriteria for manual controlof electric lighting as reportedintheliterature.Twodifferentpatternscanbeobserved.In thefirstone,theelectriclightingisswitched-onduringthehours whentheofficeisoccupied.Accordingtothismodel,duringthe occupationperiodpeoplerarelyswitch-offthelightsoncedaylight illuminancelevelsoverridethesetpointforilluminance,andmost ofthetimeoccupantsdonothavetheperceptionoftheluminaires’ state[3].Inasecondreportedmodel,thelightsareswitched-on whenthedaylightilluminancelevelisnot sufficienttoperform tasks.Generallytheelectriclightingisswitched-offwhenthe occu-pantsleavetheoffice.

Byobservingthattheoperationofelectriclightingisstrongly relatedtoilluminanceandluminancelevelsintherooms,itis rea-sonabletoassumethatthereisalsoanindirectconnectionbetween theactuationofshadingdevicesandelectriclighting.The adjust-mentoftheshadings state(increaseofopacity)tocontrolglare willresultinahighprobabilityofswitchingonthelights(ifbefore switchedoff).Logicwouldindicatea lowchanceof theinverse happeningi.e.deactivatingshadingand consequentlyswitchoff electricallighting,butnoreferenceswerefoundaboutthisspecific behavior.

Themanualoperationofshadingdevicesandelectriclighting hasbeenobjectofseveralstudiesanddifferentconditionswere reportedaspossibleoptionstomodelthebehaviorofoffice build-ingoccupants.Twentymodelsorcriteriawerefoundthatrelate tothemanualoperationofshadingdevicesandeightmodelsor criteria thatrelate tothe manualoperationof electric lighting, withalargediversityofcontrolapproaches(controlparameters, thresholds,typeofcriteriaandapplicability).Theindicated condi-tionsarenotnecessarilycompatibleamongsteachotherandmight

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Table4

Reviewofthecriteriaformanualcontrolofshadingdevicesinofficebuildings.

Drivingparameter Source Criteriaforadjustmentoftheshading position

Action Developmentcontext

Opening Closing Indoorilluminance [5,37] ShadeactuatedifEWorkplaneDaylight

higherthan1800lux

× Surveyof16officebuildings.U.K.

[2] Actionsonarrival(lowerblinds) Probabilityofwindowblindclosingas afunctionofindoorilluminanceand lowerunshadedwindowfraction (combined5000luxand100%resultin aprobabilityofactionof81%)

× Monitoringof14oneortwo-person

officesduring7years.Switzerland

[2] Actionsonarrival(lowerblinds) Probabilityofwindowblindopeningas afunctionofindoorilluminanceand lowerunshadedwindowfraction (combined500luxand0%resultina probabilityofactionof50%).

× Monitoringof14oneortwo-person

officesduring7years.Switzerland

[2] Actionsduringthepresenceandat departure(lowerblinds)

Probabilityofwindowblindclosingas afunctionofindoorilluminanceand lowerunshadedwindowfraction (combined5000luxand100%resultin aprobabilityofactionof19%)

× Monitoringof14oneortwo-person

officesduring7years.Switzerland

[2] Actionsduringthepresenceandat departure(lowerblinds)

Probabilityofwindowblindopeningas afunctionofindoorilluminanceand lowerunshadedwindowfraction (combined500luxand0%resultina probabilityofactionof16%)

× Monitoringof14oneortwo-person

officesduring7years.Switzerland.

Luminance [25] Inferredthatshadingisactuatedifany

LVisualfieldhigherthan1000cd/m2

× Undetermined

[41] InferredthatshadingisactuatedifL

Windowhigherthan5000cd/m2

× Undetermined

[6] Actionsonarrival

Probabilityofwindowblindclosingas afunctionofmaximumwindow luminance(4466cd/m2fora

probabilityofactionof50%), backgroundluminance(225cd/m2fora

probabilityofactionof50%),and averagewindowluminance(890cd/m2

foraprobabilityofactionof50%),

× Surveyof2officebuildingsoffices

during5months.California,U.S.

[7] ShadingactuatedifLWindowhigherthan

1800cd/m2

× Monitoringof8onepersonoffices

during7months.France

Glareindexes [8] ShadingactuatedifDGIhigherthan20 × undetermined

[39] InferredthatshadingisactuatedifDGI higherthan24

× undetermined

[9,49] ShadingactuatedifDGPhigherthan 40%

× Experimentalstudyandoccupants

survey(70subjects).Denmarkand Germany

Solarradiationand externalilluminance

[13] Shadeactuatedifintensityofdirect normalsolarradiationhittingthe occupantshigherthan233W/m2

× Undetermined

[8] Shadeactuatediftransmitteddirect solarradiation(throughthe transparentfac¸ade)higherthan 94.5W/m2

× Undetermined

[10,17] Shadingactuatedifverticalsolar irradiationhigherthan300W/m2

× Undetermined

[11] Shadingactuatedifthedirectsolar radiationthathitstheworkplace higherthan50W/m2(consideringa

specifieddeptintotheroomof1m)

× Monitoringandoccupantssurveyof4

officebuildingsduring3weeks.Japan

[4] Shadingactuatedifdirectsunlight higherthan50W/m2andsolargains

higherthan50klux(450W/m2)

× Monitoringof10oneandtwo-person

officesduring9months.Germany

[4] Shadingactuatedifilluminanceon

externalfac¸adehigherthan25klux

× Monitoringof10oneandtwo-person

officesduring9months.Germany

[6] Actionsonarrival

Probabilityofwindowblindclosingas afunctionoftransmittedverticalsolar radiation(13W/m2foraprobabilityof

actionof50%)

× Measurementsandoccupantssurvey

of2officebuildingsofficesduring 5months.California,U.S.

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Table4(Continued)

Drivingparameter Source Criteriaforadjustmentoftheshading position

Action Developmentcontext

Opening Closing

[60] Meanshadedeploymentdegree

(m.s.d.d.)asafunctionofincident verticalsolarirradiation(South 250W/m2m.s.d.d.=75%;North

250W/m2m.s.d.d.=13%;South-west

andsouth-east

250W/m2m.s.d.d.=62%;North-west

andnorth-east250W/m2m.s.d.d.=19%;

Westandeast250W/m2m.s.d.d.=50%)

× × Monitoringof5officebuildings(163

workplaces)during9–14months. Austria

significantlyimpacttheoverallenergyconsumptionofoffice build-ingswhenusedinintegratedbuildingsimulation.

4. Sensitivityanalysisforanofficecasestudy

Theliteraturereviewfromtheprevioussectionsshowedthat therearedifferentcontrolmodelsofhowpeopleinteractwith shad-ingdevicesandwithelectriclighting.Theseinteractionsinturn influencebuildingperformanceintermsoftheirenergydemand forheating,coolingandartificiallighting.Itisthereforeimportant toassesshowthechoicesofbehavioralmodelsmightinfluencethe forecastedenergydemand.Itisalsoimportanttoassesswhether thedifferencesinenergyconsumptionpredictionresultingfrom theselectionofa behavioralmodelhaveanimpactonthe sub-sequentrankingofdesignalternativesforthetransparentfac¸ade elements(e.g.theglazingtype,theshadingdeviceoreventhearea oftransparentsurface).Ifthisisthecase,thenincreasingthe robust-nessofexistingbehavioralmodels–ordevelopingamorerobust methodtochooseamongstthemodels–wouldbecomeapriority forreliableenergysimulations.

So as to assess the impact of the behavioral model choice (regardinginteractionwithshadingdevicesandelectriclights)on

theenergyperformance ofbuildings, adetailed studywas per-formedtakingasreferenceasingle-occupantofficeroomlocated in Porto,Portugal. Porto is located atthe latitudeof 41◦N and hasanEuropeanAtlanticClimate,with1610heatingdegree-days at base temperature of 20◦C. The room (Fig. 1) was modeled inwhole-buildingsimulationsoftware,anddifferenttransparent fac¸adedesignoptionsweretested.Amoredetaileddescriptionof thebuildingandroomarepresentedinSection4.1.

Fig.2summarizestheframeworkoftheconductedsimulations. Thisframeworkincluded:Threescenariosregardingtheresultof climate and building characteristics: a heating-dominated sce-nario,acooling-dominatedscenarioandonewithbalancedheating and cooling.Thedifferentiationwasachievedbyincreasingthe levelofthermalinsulationoftheenvelope(toachievea cooling-dominated scenario) orby loweringthe airtemperature ofthe climatefile(toachieveaheating-dominatedscenario);Foreachof thescenariosdescribedin(i),theanalysisoffouroptionsregarding thechoiceofglazing,fouroptionsregardingtheshadingdevices, and four options regarding the area of transparent fac¸ade;The energydemandwitheachoftheglazing,shadingorwindowareas describedin (ii)wascalculatedfor 11behavioralmodelsofthe interactionbetweentheoccupantsandtheshadingdevices.

Table5

Criteriaofmanualcontrolofelectriclightinginofficebuildings.

Driving parameter

Author Criteriaforadjustmentoftheelectriclighting state

Action Developmentcontext

Switching-on Switching-off Illuminance [61] Probabilityoflightsbeingswitchedon

functionofexternaltotalilluminance(ina multi-personoffice)(36kluxforaprobability ofactionof50%)

× Monitoringof3multi-person

officesduring6months.U.K.

[3] Actionsonarrival

Probabilityoflightsbeingswitchedonasa functionofindoorilluminance(67luxfora probabilityofactionof50%)

× Monitoringof3multi-person

officesduring6months.U.K.

[4] Actionsonarrival

Probabilityoflightsbeingswitchedonasa functionofindoorilluminance(170luxfora probabilityofactionof2%)

× Monitoringof10oneor

two-personofficesduring 9months.Germany

[4] Actionsduringthepresence

Probabilityoflightsbeingswitchedonasa functionofindoorilluminance(67luxfora probabilityofactionof50%)

× Monitoringof10oneor

two-personofficesduring 9months.Germany

Luminance [27] Inferredthatelectriclightingstateisadjusted

ifLCeilinghigherthan850cd/m2

× Occupantspreferences

[35] Inferredthatelectriclightingstateisadjusted ifLCeilinghigherthan500cd/m2

× Occupantspreferences

Periodofabsence [14] Probabilityoflightsbeingmanuallyswitched offfunctionoflengthofoccupancyabsence fromtheworkstation(range1–2hfora probabilityofactionof38%)

× Monitoringof63oneperson

officesduring11months. Winscosin,U.S.

[12,60] Probabilityoflightsbeingmanuallyswitched offfunctionoflengthofoccupancyabsence fromtheworkstation(125minfora probabilityofactionof50%)

× Monitoringof5officebuildings

(163workplaces)during9to 14months.Austria

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Fig.1.Casestudy–singleofficeroom.

Foreach combinationof scenarioand designalternative,the electriclightingandspaceheatingandcoolingneedswere calcu-latedthroughdynamicbuildingsimulation,usingthePorto’shourly weatherdatafile.Ineachofthesimulationselectriclightingwas consideredtobecontrolledwithanidealdimmingcomplementary tonaturallighting, inordertoensure500luxontheworkplane duringtheoccupiedperiod.

4.1. Geometryandenvelope

Theofficeroomis5.3mlongand3.0mwide,resultinginafloor areaof16m2.Theroomisdaylitbyasouthorientedwindowof 5.5m2,whichfillsabout65%oftheofficesouthfac¸ade.Theonly elementsof theoffice envelopethat contact withtheoutdoors arethesouthfac¸ade(8.5m2).TheexternalwallshaveaU-value of0.67W/m2◦C.Alltheotherelementsoftheofficeenvelopein contactwithotherofficeroomswereassumedtobeatthesame indoorenvironmentalconditions.Thewindowisshadedby exter-nalVenetianblinds.Theroomoccupancywasdefinedas1person from9to13handfrom14to19h,theartificiallightselectricpower as10W/m2from9to19h,theofficeequipmentselectricpoweras 10W/m2from9to19h,andthefreshairventilationrateas1ach from9to19hand0.5achfrom0to9handfrom19to24h.

Theassessmentoftheofficeoverallenergyconsumption(for lighting,heatingandcooling)wasperformedconsidering differ-entdesignalternativesforthetransparentfac¸ade:glazingtypes, windowtowallratiosandshadingdevices.

Asfarasglazingsareconcerned,therangeofanalyzed alter-nativesconsistedoffourdifferentdoublelayerglazingwindows (6mm-12mm-6mm),whosemainpropertiesareshowninTable6. Theseexhibitsimilarcoefficientsofheattransmissionbutdifferent opticalvalues,coveringtheoverallrangeofexistingglazingoptical

characteristics,fromadarksolarcontrolglazing(G1)toaveryclear glazings(G4)[62].Itwasconsideredthatalloftheseglazingshave veryhighcolorrenderingindexesandtherefore,neutralimpacton colorperception.

Fourdifferentvaluesofwindowtowallratio(WWR– percent-ageoftheexteriorwallareawhichismadeofwindow)weretested, 20%,40%,65%and90%,alwayskeepingthecenterofwindowatthe occupantsviewlevel.Table9showsthepropertiesofthe consid-eredWWRalternatives.

The propertiesof thefour optionsfor the external venetian blindsconsideredforthisstudyweretakenfromthecalculation toolWIS[63]andarepresentedinTable8.Similarlytothe glaz-ings,thefourshadingdeviceoptionswereselectedbasedontheir transmittancevalues.

Asmentionedintheintroductionofthissection,asensitivity analysisofTables6–8wasperformedthreetimes:firstina sce-nariowheretheannualcoolingdemandissignificantlyhigherthat theheatingdemand,secondwithtwodemandsthatareofthesame orderofmagnitudeandthirdwithacoolingdemandsignificantly lowerthantheheatingdemand.Thecooling-dominatedscenario correspondstothe“asbuilt”inPorto,consideringoneexteriorwall, South-oriented,withanU-valueof0.48W/(m2◦C).The transfor-mationtoward“balancedheatingandcooling”wasachievedby consideringwallsandroofasexternal(U-valuesof0.48W/(m2◦C) and0.27W/(m2◦C),respectively),whichincreasedtheheatlosses towardoutdoors;andfinally,toachieveaheating-dominated sce-nario,theenvelopeofthebalancedheatingandcoolingscenario wasconsideredhowevertheclimatefilewaschangedby arithmeti-callyremoving8◦Cfromtheoutdoortemperatureintheclimatefile forallhoursoftheyear.Thisvirtualclimatefileoptionwas consid-eredpreferabletochangingtheclimatelocation,asthelatterwould affectalsothedaylightingavailabilityandconsequentlydecrease thecomparabilityoftheresults.

4.2. Behavioralmodels/controlstrategiesoftheshadingdevices Themain objectiveofthis workistoassesstheimpactthat behavioral models and associated control patterns for shading deviceshaveintermsofenergydemandforheating,coolingand electriclighting.Therefore,followingthereviewpresentedin Sec-tions 2 and 3, a comprehensive set of behavioral models was selectedtobesubjectedtoasensitivityanalysis.Thesetis essen-tiallytheonealreadypresentedinTable4,plusfoursteady-state strategies.TheyareallsummarizedinTable9asasinglelist.

ThestrategiesS0(blindstotallyinactive)andS1(blindstotally active)arenotrepresentativeofrealpatternsofblindscontrolbut wereconsideredaslimitconditions.

S2andS3arethestrategiesmostoftenconsideredinpractice bybuildingdesignersandconsultantswhenassessingtheenergy performanceofbuildings.Theysometimesareimposedbythe cal-culationmethodsadoptedbynationalregulations[15].

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Table6

Transparentfac¸ade–glazingtypesalternatives.

Glazingproperties Shadingdevice WWR

U-valueW/(m2◦C) g-Value(%) TSol(%) TVis(%)

G1 1.57 20 3 6

Shad3(TSol=24.0%;TVis=24.9%) WWR3(65%)

G2 1.57 30 15 24

G3 1.57 51 41 43

G4 1.58 75 63 79

Table7

Transparentfac¸ade–WWRalternatives.

WWR(%) Glazingtype Shadingdevice

WWR1 20

G3(TSol=41.0%,TVis=42.0%) Shad3(TSol=24.0%;TVis=24.9%)

WWR2 40

WWR3 65

WWR4 90

ThestrategiesS4,S5,S6andS7arebasedontheassumptions thatoccupantsactivateexternalblindseachtimetheyarevisually uncomfortable,andthattheyturntheblindsinactiveagainassoon astheoccupantsarecomfortablewithouttheactionoftheblinds. TwodifferentthresholdvaluesfortheDGIwereconsidered(16and 22).RecognizingthatDGIishighlydependentontheoccupantview angle,twodifferentpositionswereconsideredfortheoccupant: astandardoccupantpositionthatwouldrepresentaviewangle paralleltothewindow(0◦)andasecondonewhichconsidersthe occupantslightlyturnedtowardthewindow(viewangleof20◦ withthewindowplane).

ThestrategiesS8andS9makeuseoftheverticalsolar radia-tiontransmittedthroughthewindowtoadjustblindspositions. Thevalueofbeam(directnormal)plusthediffuseradiationis cal-culatedforeachsimulationtime-stepandtheblindisfullylowered ifthetransmittedsolarradiationthrough thewindow (without theeffectoftheshadingdevice)ishigherthanaspecifiedsolar radiationvalue(100W/m2 in S8and200W/m2 inS9).Foreach

glazingtypetheglazingsolartransmittancewasconsideredtofind theincidentsolarradiationthatshallbeusedtotriggertheblind position.

ThestrategiesS10andS11relyontheuserbehavioralcontrol modelLightswitch[22,64].ThismodelisintegratedinDAYSIM[65]. Thetwodifferenttypesofuserbehaviorweretested:theactive (S10)andthepassive(S11).Theybothwillpartlyclosetheshading devicesduringthedaytoavoiddirectsunlighthigherthan50W/m2 impingingtheworkingspace.Onceclosed,theywillremaininthat state.Themaindifferencebetweenbothtypesisthattheactive typefullyopenstheshadingdeviceswhenarrivinginthemorning, whilethepassivetypedoesnotdothis.

4.3. Estimationoftheoverallenergyconsumption

AllsimulationswereperformedwiththeEnergyPlusbuilding simulationsoftware[19].Itallowsforthedirectimplementation ofthecontrolstrategiesS0toS9.ForstrategiesS10andS11,the DAYSIM[65]outputtextfiles(hourlyvaluesofindoorilluminance, electriclightingandshadingdevicesstate)wereusedasinputsto

EnergyPlustospecifythestateoftheblindateachhouroftheyear, thusensuringtheconnectionofbothalgorithms.Thethermalpart wastreatedbyEnergyPlusalone.

Allsimulationswereperformedwithanidealdimmingstrategy tocontrolelectriclightingtopreventpossibleoverlappingbetween manualoccupants’modelstocontrolelectriclightingandmodels tocontrolshadingdevices.Thismeansthatthelightpowerofthe officeroomiscontinuouslyadjustedbyavirtualreal-time ideal-dimmingsystem,whichreducestheelectricpowerproportionally totheamountofincident daylighttoguarantee a minimumof 500luxonoftheworkplane,positioned2mawayfromthewindow. ThecalculationofthedaylightcontributionisbasedonDaylight Factorsfor a standard overcast skyand onindoor andoutdoor illuminancerationforclearskyconditionswith20different repre-sentativesunpositionsofoneindoorreferencepoint[66,67].The modelsusingthecontrolstrategies S10andS11usetheindoor illuminancecalculatedbyDAYSIM,asreferredpreviously.DAYSIM hasanintegratedRADIANCEalgorithm coupledwitha daylight coefficientapproachtoestimatedaylightilluminances[65].

Foreachsimulation,thetotalannualenergyconsumptionfor electriclighting,ambientheatingandambientcoolingwas evalu-atedbasedonthebuildingdesignoptions,thecontrolstrategiesand theboundaryconditionsdescribedpreviously.Toestimatethefinal energyconsumptionforspaceheatingandcoolingtheindoor tem-peraturewasassumedtobecontrolledduringofficeroomworking hours(9–19h),andkeptataminimumof20◦Cduringthe heat-ingseasonandamaximumof24◦Cduringthecoolingseason.The energyconsumptioniscomputedintheformofelectricity, assum-ingthattheheatingandcoolingareprovidedbyareversibleheat pumpsystemwithaseasonalCOPof4andaEERof3,respectively.

5. Results

5.1. Coolingdominatedscenario

Inthecontextofthecooling-dominatedscenario,Fig.3shows theannualenergyconsumptionoftheofficeroom,forthefour glaz-ingalternativesG1toG4presentedearlierinTable6,undereach

Table8

Transparentfac¸ade–shadingdevicesalternatives.

Shadingdeviceproperties Glazingtype WWR

TSol(%) TVis(%) Shad1(venetianblinds) 11.9 11.9

G3(TSol=41%,TVis=42%) WWR3(65%) Shad2(venetianblinds) 16.8 17.7

Shad3(venetianblinds) 24.0 24.9 Shad4(venetianblinds) 29.5 31.7

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Table9

Behavioralmodels/Shadingdevicescontrolstrategiestobeconsideredinthe sensi-tivityanalysis.

S0 Noshading(shadingfullyopenallyearround) S1 Shadingis100%activefromJanuarytoDecember S2 Shadingis70%activefromApriltoSeptember S3 Shadingis100%activefromApriltoSeptember

S4 ShadingisactiveifDGI>20(GI>16),0◦(viewdirectionparallel towindow)

S5 ShadingisactiveifDGI>20(GI>16),20◦(viewdirection20◦ towardwindow)

S6 ShadingisactiveifDGI>24(GI>22),0◦(viewdirectionparallel towindow)

S7 ShadingisactiveifDGI>24(GI>22),20◦(viewdirection20◦ towardwindow)

S8 Shadingisactiveiftransmittedverticalbeamplusdiffusesolar radiation(SRTr)>100W/m2

S9 ShadingisactiveifSRtr>200W/m2

S10 Shadingisactiveifdirectsunlightthathitstheworkplane >50W/m2,activeuser(Lightswitch)

S11 Shadingisactiveifdirectsunlightthathitstheworkplane >50W/m2,passiveuser(Lightswitch)

ofthe12shadingcontrolstrategiesS0toS11describedinTable9. TheenergyconsumptionforeachscenarioispresentedinkWhper yearperm2ofofficefloorarea(Fig.4).

Fig.5showstheresultsforthefourshadingalternatives pre-sented in Table 7, while Fig. 6 shows the results for the four alternativesofWWRdescribedinTable8.

In cooling-dominated scenario, results show that different behavioral models result in differentchoices of a “best design alternative”. Thedifferences are mostnoticeable for thechoice ofglazing alternativesand WWRalternatives,highervariations ofoverallenergyconsumptionarealsoobservedwhencompared totheenergyconsumptionassociatedwithshadingalternatives. Thisissomewhatnatural,sincethedifferencesamongstthe glaz-ingandWWRvaluesarealsobiggerthanforshading(seeTable7). TheshadingscontrolstrategiesS0andS1areindicatedas refer-ences,toassisttheanalysisofstrategiesS2toS11.Overallresults presentaconsiderabledispersionintermsofenergyconsumption, withsignificantdifferencesbothbetweendesignalternativesand betweenthetwelveshadingcontrolstrategies.Whiletheranking ofthealternativesisgenerallynotsignificantlyinfluencedbythe changeofcontrolstrategy,thereareexceptionsforstrategiesS1, S8andS11,whichallleadtoshadingsbeingactiveduringmost ifnotallofthetime.Furthermore,thelowertransmittancesand wallareasarelesssensitivetochangesincontrolstrategy.Thetwo variantsoftheLightswitchbehavioralmodelproducesignificant rankingvariations.Thesearejustifiedbythefundamental differ-encesinthealgorithms:withthepassiveusermode,theshadings arefullydeployedalmostalwaysthroughouttheyear.

Fig.3.Overallenergyconsumptionforthefourglazingalternativesineachofthe 12controlstrategies.

5.2. Heatingdominatedscenario

Fortheheating-dominatedscenario,Fig.7shows theannual energyconsumptionoftheofficeroom,forthefourglazing alter-nativesG1toG4undereachofthetwelveshadingcontrolstrategies S0toS11.Fig.8showstheresultsforthefourshadingalternatives presentedinTable7,whileFig.9showstheresultsforthefour alternativesofWWR.

In this heating-dominated scenario, the ranking of design alternatives remains nearly constant across the different shad-ingdevicescontrolstrategies.Theexceptionsaretheparametric studiesrelatedtotheglazingandthoserelatedtotheshading trans-mittanceinthosecaseswheretheLightswitch-activeusermodel leadstosomerankinginversion.Therelativelysmallinfluenceof thecontrolstrategiesinthisscenarioisalsoconfirmedbythefact thattheenergyconsumptionofeachdesignalternativetendstobe similaracrossthedifferentcontrolstrategies.Anexplanationfor thisisthefactthatthedecreaseinelectriclightingneedsdecreases ispartiallyoffsetbyanincreasedneedforheating.

5.3. Balancedheatingandcoolingscenario

Theannualenergyconsumptionoftheofficeroomforthecase whenheatingandcoolingenergyneedsarecomparable,isshown inFig.10forthefourglazingalternatives.Fig.11showstheresults forthefourshadingalternativesandFig.12showstheresultsfor thefourWWRalternatives.

In this scenario, a reasonable number of ranking inversions occuracrosscontrolstrategies. ThestrategiesS9andS10imply rankingvariationsifcomparedwiththerankingresultingfromthe DGIbasedcriteria (S4toS7)and theLightswitchcriteria(S10), butaresimilartotherankingoftheseasonal-basedstrategies(S2 andS3).Asinthepreviousscenarios,theinfluenceofthecontrol strategies ontheenergyconsumption ismorenoticeablein the simulationoftheglazingtransmittanceandwindowtowallratio alternatives.

6. Resultsanalysis

Themainpurposeofthiscasestudyistoassesstheimpactof theshadingscontrolstrategy(whichsupposedlymimicsthehuman behavior)ontheselectionoftransparentfac¸adealternatives,when usingtheoverallconsumptionoffinalenergyforheating,cooling andlightingasthedecisioncriteria.TheresultsdiscussedinSection 5doshowthatshadingcontrolstrategiesinfluencethechoiceof thebestdesignalternative.

Inordertoanalyzeinasystematicwayhowtheenergyresults anddesignoptionsareaffectedbythecontrolstrategiesand behav-ioral model, a setof indicators werecomputed (Table 10):Best alternative:percentageofoccurrences(controlstrategies)inwhich the design alternative leadstothe lowest energy consumption amongthealternativesanalyzed;Averageranking:average posi-tionindexofeachalterativeconsideringallthecontrolstrategies (where,foreachcontrolstrategy,thepositionindexis1forthe alternativewhichresultsinthelowestenergyconsumptionand 4thealternativewhich resultsinthehighestenergy consump-tion);Ranking ratio (max/min): ratio between the highest and lowestpositionindexofeach designalternativeintherangeof controlstrategiesanalyzed.Arankingratioof4.0thereforemeans thatthealternativehasatleastonecontrolstrategyinwhichitis thebestdesignoptionandatleastonecontrolstrategyinwhich istheworstoption.Arankingratioof1.0indicatesthatthe rank-ingpositionofthealternativeisconstantthroughallthecontrol strategies(regardlessofrankingposition).

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Fig.4.Yearlyenergyconsumptiondisaggregationforthefourglazingtransmittancealternatives.

Table10showstheindicatorscomputedforeachtransparent envelopedesignalternative(glazing,shadingandwindow-to-wall ratio).Thecalculationoftheindicatorsdidnottakeinto considera-tiontheshadingscontrolstrategiesS0andS1,sincethosestrategies considerapermanentstateoftheshadingsduringalltheyear(they werestudiedforreferencecomparisononly).

TheindicatorsshowninTable10confirmthattheenergy-based meritofa givendesign alternativevariesconsiderablywiththe controlstrategyassumedinthesimulation.Fromthe9setsof case-studiesanalyzed,onlyinonecase(variationofthewindow-to-wall ratiointheheatingdominatedscenario)wasthereadesign alterna-tive(WWR4)whichwasalwaysthebestunderanyofthecontrol strategiesconsidered(S2toS11).Furthermore,thereisa signif-icantnumberofdesignalternativesthathavearankingratioof 4.0,meaningthattheyareconsideredthebestwithsomecontrol

strategy(ies)andtheworstwithsomeothercontrolstrategy(ies). Thetablehoweveralsorevealsthatthedispersionismuchmore significantinthe“balancedheatingandcooling”scenariothanin the“coolingdominatedscenario”andespeciallythaninthe “heat-ingdominatedscenario”.Inthislatterone,theidentificationofthe bestalternativeisalmostconsensual,withatleast90%ofthe con-trolstrategiesleadingtothesamechoiceofbestalternative.Inthe “balancedheatingandcooling”scenariohoweverthereisacasein whichthebestalternativeisnotrecognizedassuchbyasmuchas 40%ofthecontrolstrategies.

Thefactthattheuseofdifferentcontrolstrategiesmaylead tothechoiceofdifferentdesignsolutionsisinconvenientfroma practicalpointofview.Itmayalsomeanthatmanydesign solu-tionschosentodayasenergyefficientmayinfactnotbeso.This justifiestheneedforfurtherresearchinthisarea,whichmaylead

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Fig.5.Overallenergyconsumptionforthefourshadingtransmittancealternatives ineachofthe12controlstrategies.

Fig.6.Overallenergyconsumptionforthefourwindowtowallratioalternatives ineachofthe12controlstrategies.

tobehaviormodelswithhigherstatisticalsignificancesoastobe widelyacceptableasrepresentativeofoccupants’behavior.

Whilesuchmodelsarenotavailable,andgiventhatthereisno scientificallysolidindicationthatanyofthebehavioralmodelsor controlstrategiesisclearlybetterthantheothers,apossible solu-tionrecommendablefromamethodologicalpointofviewwouldbe toconsiderallthecontrolstrategiesandchoosethedesign alter-nativethatranksbestaccordingtotheaverage.Avariantofthis methodwouldbetochooseadesignalternativethatrankswell andhasalowrankingratio(meaningthatevenifnotguaranteeing thebestperformance,itwouldneverleadtopoorperformance).

However,intheregularpracticeofbuildingdesign,itisnot con-venienteither–iffeasibleatall–tosimulatethebuildingwith

Fig.7. Overallenergyconsumptionforthefourglazingtransmittancealternatives ineachofthe12controlstrategies.

Fig.8. Overallenergyconsumptionforthefourshadingtransmittancealternatives ineachofthe12controlstrategies.

Fig.9.Overallenergyconsumptionforthefourwindowtowallratioalternatives ineachofthecontrolstrategies.

differentdesignsolutionsanddifferentcontrolstrategiesto per-formachoiceanalysisafterthat.Infact,asreferredtoinSection 1,consideringonedynamiccontrolpatternisalreadyaprocedure ontheadvanced sideofthebuildingdesignpractice.Therefore, asearchforthemostreliablecontrolstrategyisrequired,i.e.are whichwouldbemostlikelytoleadtothechoiceofthesamedesign alternativeastheprocedurebasedonthe“bestaccordingtothe averageofthecontrolstrategies”would.Thiswouldindeedallow tosimulatewithjustonecontrolstrategyinsteadofthenine con-trolstrategiesinitiallyconsidered.Table11providesalistof“the bestaccordingtotheaverage”and“thebestaccordingtoeach indi-vidualcontrolstrategy”.Thelastlineofthetablealsoshowsthe

Fig.10.Overallenergyconsumptionforthefourglazingtransmittancealternatives ineachofthecontrolstrategiesalternatives.

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Fig.11.Overallenergyconsumptionforthefourshadingtransmittancealternatives ineachofthecontrolstrategiesalternatives.

percentageofexactmatchesbetweentheaveragerankingofthe designalternative andtherankingaccordingtoeach individual controlstrategy.

TheresultsofTable11revealthatdoingthebuilding simula-tionwiththecontrolstrategyS5alwaysleadstothechoiceofthe designalternativethatisalsothebestaccordingtotheaverageofall controlstrategies(100%match).Asfarastherankingofalldesign alternativesisconcerned(andnotjustthebest),strategyS5isalso theonethatbetterreplicatestheresultsobtainedfromthe aver-ageofallcontrolstrategies,withacorrespondenceof83%.Fig.13 illustratesinagraphicalformthecorrelationbetweentherankings producedbystrategiesS5andS11andtheaveragerankingfromall

Fig.12.Overallenergyconsumptionforthefourwindowtowallratioalternatives ineachofthecontrolstrategies.

controlstrategies.Itagainreinforcestheconclusionthatstrategy S5isthestrategythatbettermimicsthechoiceoftheaverage.

In summary, this sectionprovidesan analysisof theoverall energyconsumptionresultingfromtheuseoftwelveshading con-trolstrategiesfromthepointofviewoftransparentfac¸adedesign choice.Thedesignalternativesthatwouldleadtothelowestenergy consumptionwereidentified, foreach scenario,consideringthe averageofthecontrolstrategiesandtherankingdispersion asso-ciatedtoeachstrategy.StrategyS5wasshownasbeingcapable ofidentifyingdesignoptionsthatmatchtherankingforallcontrol strategiesin83%ofthecases.

Table10

IndicatorsforeachdesignalternativeintherangeofcontrolstrategiesS2toS11.Shadowedboxesshowthealternativeswithlower(best)averageranking.

Coolingdominatedscenario Heatingdominatedscenario Balancedheatingandcoolingscenario Best alternative(%) Average ranking Rankingratio (max/min) Best alternative(%) Average ranking Rankingratio (max/min) Best alternative(%) Average ranking Rankingratio (max/min) G1(ST=3%) 0 2.8 2.0 10 3.7 4.0 0 3.8 1.3 G2(ST=15%) 80 1.3 3.0 0 3.0 2.0 40 1.8 3.0 G3(ST=41%) 10 2.3 3.0 0 2.2 1.5 40 1.7 3.0 G4(ST=64%) 10 3.6 4.0 90 1.1 2.0 20 2.7 4.0 Shad1(ST=12%) 10 3.0 4.0 0 3.8 2.0 10 3.5 4.0 Shad2(ST=17%) 60 1.6 3.0 10 2.8 3.0 10 2.7 3.0 Shad3(ST=24%) 20 2.1 3.0 0 2.1 1.5 0 2.2 1.5 Shad4(ST=30%) 10 3.3 4.0 90 1.3 4.0 80 1.6 4.0 WWR1(20%) 30 2.1 4.0 0 4.0 1.0 0 3.3 2.0 WWR2(40%) 60 1.5 3.0 0 3.0 1.0 50 1.8 3.0 WWR3(65%) 0 2.8 1.5 0 2.0 1.0 40 1.8 3.0 WWR4(90%) 10 3.6 4.0 100 1.0 1.0 10 3.5 4.0 Table11

Comparisonofthebestalternativeforeachcontrolstrategywiththeaveragebestalternative.

Designalternatives Opaqueenvelopescenarios Bestalternative Bestalternativesforeachcontrolstrategy

S2 S3 S4 S5 S6 S7 S8 S9 S10 S11

Glazingtypes Coolingdominated G2 G2 G2 G2 G2 G2 G2 G3 G2 G2 G4

Heatingdominated G4 G4 G4 G4 G4 G4 G4 G4 G4 G1 G4

Balancedcoolingandheating G3 G3 G3 G2 G3 G2 G2 G3 G4 G2 G4

Shadingdevices Coolingdominated Shad2 Shad2 Shad4 Shad3 Shad2 Shad3 Shad3 Shad3 Shad1 Shad1 Shad3 Heatingdominated Shad4 Shad4 Shad4 Shad4 Shad4 Shad4 Shad4 Shad4 Shad4 Shad2 Shad4 Balancedcoolingandheating Shad4 Shad1 Shad4 Shad4 Shad4 Shad4 Shad4 Shad4 Shad4 Shad2 Shad4 WWR Coolingdominated WWR2 WWR2 WWR2 WWR1 WWR2 WWR1 WWR1 WWR2 WWR2 WWR2 WWR4

Heatingdominated WWR4 WWR4 WWR4 WWR4 WWR4 WWR4 WWR4 WWR4 WWR4 WWR4 WWR4 Balancedcoolingandheating WWR2 WWR3 WWR3 WWR2 WWR2 WWR2 WWR2 WWR3 WWR3 WWR2 WWR4 Percentageofmatcheswiththebestalternative 78% 78% 67% 100% 67% 67% 67% 56% 44% 44% Percentageofmatchesoftheaveragerankingwiththedesign

alternativeranking

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Fig.13.RankingswithcontrolstrategiesS2andS11vs.theaverageranking(numberofpointsoverlappedindicatedinsidethecircles).

7. Conclusions

Thisworkstartedwithareviewestablishingthehuman require-mentsforvisualcomfortinofficebuildingsandtheconditionsthat promptbuildingoccupantstointeractwiththeshadingdevices andwiththeelectriclights.Numerouspublications werefound addressingthepreferencesofworkersregardinglighting condi-tionsinoffices.Thereisalargerangeofreportedparametersand even divergent recommendations among the different sources, thoughallclaimtorepresentoccupantspreferencesinanoffice building. Workplane illuminanceis the mostcited and studied parameter.Someotherparametersreferredtoaresurface) lumi-nance (ofwindows, walls and ceiling), solar radiation entering thespace and daylight discomfortindexes. However, theseare notconsistently presentedintheliterature,sofurther develop-mentsare necessaryto reliablycharacterize occupants comfort preferencesandresponsetolightingconditionsinordertoallow forrobustcomparisons.Regardingmanualoperationofwindow movableshadingsorthechangeoftheartificiallightingstate,a sig-nificantnumberofoccupantbehavioralpatternsandmodelswere foundtoo,withsignificantdiscrepanciesamongthebibliographic sources,especiallyintermsofshadingdevicescontrol.

Thesimulationcasestudycarriedouttounderstandtheimpact oftheuseofdifferentcontrolpatternsandbehavioralmodelson theselectionoftransparentfac¸ade designoptionsrevealedthat thesedirectlyinfluencetheselectionofdesign alternatives.The resultsshowedthatdifferentbehavioral models,evenifall doc-umentedinthescientificliterature,resultindifferentchoicesof “bestdesignalternative”.Thiswasremarkableontheofficewith balanced heatingand cooling loads, and tosomeextent inthe cooling-dominatedandintheheating-dominatedscenarios. Fur-thermore,theresultsshowthatevenwhennotchangingthemerit orderofdesignalternatives,theconsiderationofdifferentcontrol patternsand behavioralmodelshasa significantimpactonthe computedenergyperformance.

Sincedifferentbehavioral modelsleadtodifferentchoicesof bestdesignalternativesbutthereisnoclearindicationofwhich behavioralmodelsarebest,theoptionofchoosingadesign alter-nativebasedonaveragerankingcomputedwiththeresultsofall behavioralmodelswasconsidered.Theresultsshowthatthe con-trolstrategyS5(shadingisactiveifDGIishigherthan20)isthe one that,considered alone,replicatesmost reliably thechoices madewiththeaverageranking.Regardingthechoiceofthebest designalternative,choosingwithS5alonealwaysledtothesame choiceaschoosingwiththeaverage.Thisresultshallnotbe inter-pretedasprovingthatthebehavioralmodelS5ismorerealistic orvalidthantheothers:itjustmeansthatitbetterrepresentsthe averageofthewholegroupofmodelsconsideredintermsofthe resultsthattheyproduce.Therefore,thisstudyreinforcestheneed

offurtherresearchfocusedontheidentificationofbehavioral mod-elswithhighstatisticalsignificance,developedfromcampaignsof post-occupancymonitoringofalargenumberofofficebuildings. Acknowledgments

ThisresearchwassupportedbythePortugueseFoundationfor Scienceand Technology(Fundac¸ãopara aCiênciae Tecnologia– FCT),underprojectPTDC/ENR/72597/2006“Integratedevaluation oftheimpactofglazingandshadingdevicesontheenergy per-formanceofPortugueseofficebuildings”,hostedtheInstituteof MechanicalEngineeringattheFacultyofEngineeringofthe Uni-versityofPorto.Theauthorsgratefullyacknowledgethefinancial supportoftheFacultyofEngineeringoftheUniversityofPorto,the MassachusettsInstituteofTechnologyandtheÉcolePolytechnique FédéraledeLausanne.

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