Assessing Gaps and Needs for Integrating Building Performance Optimization Tools in Net Zero Energy Buildings Design

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ContentslistsavailableatSciVerseScienceDirect

Energy

and

Buildings

jo u rn 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

Assessing

gaps

and

needs

for

integrating

building

performance

optimization

tools

in

net

zero

energy

buildings

design

Shady

Attia

a,b,∗

_,

_Mohamed

_Hamdy

c

_,

_William

_O’Brien

d

_,

_Salvatore

_Carlucci

e

a_{Interdisciplinary}_Laboratory_of_{Performance-Integrated}_Design_(LIPID),_Ecole_{Polytechnique}_Federale_de_Lausanne_(EPFL),_Switzerland b_Université_Catholique_de_{Louvain-la-Neuve,}_Architecture_et_climat,_{Louvain-la-Neuve}_1348,_Belgium

c_Aalto_University,_School_of_Engineering,_Department_of_Energy_Technology,_PO_Box_14400,_FI-00076_Aalto,_Finland d_Department_of_Building_and_Civil_&_{Environmental}_Engineering,_Carlton_University,_Toronto,_Canada

e_Dipartimento_di_Energia,_Politecnico_di_Milano,_Milan,_Italy

a

r

t

i

c

l

e

i

n

f

o

Articlehistory: Received8January2013 Accepted19January2013 Keywords: Simulation-basedoptimization Zeroenergybuildings Evolutionaryalgorithms Needs Gaps Review Interview

a

b

s

t

r

a

c

t

Thispapersummarizesastudyundertakentorevealpotentialchallengesandopportunitiesfor integrat-ingoptimizationtoolsinnetzeroenergybuildings(NZEB)design.Thepaperreviewscurrenttrendsin simulation-basedbuildingperformanceoptimization(BPO)andoutlinesmajorcriteriaforoptimization toolsselectionandevaluation.Thisisbasedonanalyzinguser’sneedsfortoolscapabilitiesand require-mentspecifications.Thereviewiscarriedoutbymeansofaliteraturereviewof165publicationsand interviewswith28optimizationexperts.Thefindingsarebasedonaninter-groupcomparisonbetween experts.TheaimistoassessthegapsandneedsforintegratingBPOtoolsinNZEBdesign.The find-ingsindicateabreakthroughinusingevolutionaryalgorithmsinsolvinghighlyconstrainedenvelope, HVACandrenewableoptimizationproblems.Simplegeneticalgorithmsolvedmanydesignand oper-ationproblemsandallowedmeasuringtheimprovementintheoptimalityofasolutionagainstabase case.Evolutionaryalgorithmsarealsoeasilyadaptedtoenablethemtosolveaparticularoptimization problemmoreeffectively.However,existinglimitationsincludingmodeluncertainty,computationtime, difficultyofuseandsteeplearningcurve.Somefuturedirectionsanticipatedorneededforimprovement ofcurrenttoolsarepresented.

1. Introduction

During thecomingyears, thebuildingdesign community at largewillbegalvanizedbymandatorycodesandstandardsthat aimtoreachnetzeroenergybuildings(NZEBs)[1–3].Therecastof theEuropeanPerformanceofBuildingsDirective(EPBD)requires all new buildings to be “nearly zero energy” buildings (nZEB) by2020,includingexisting buildingsundergoingmajor renova-tions.Asbuildingperformanceobjectivesbecomemoreambitious

Abbreviations: AEC,architectural,engineering,construction;ACOA,antcolony optimizationalgorithm;BPO,buildingperformanceoptimization;BPS,building performancesimulation;DOE,DepartmentofEnergy;EPBD,energyperformance buildingdirective;GA,geneticalgorithms;GUI,graphicaluserinterface;HVAC, heating,ventilationandairconditioning;IBPSA,internationalbuildingperformance simulationassociation;IEA,internationalenergyagency;NSGA,non-dominated sortinggeneticalgorithm;NREL,nationalRenewableEnergyLaboratory;nZEB, nearlyzeroenergybuilding;NZEB,netzeroenergybuilding;MPC,modelpredicted control;SQP,sequentialquadraticprogramming;WWR,window-to-wallratio.

∗ _{Corresponding} _author_at:_{EPFL-ENAC-IA-LIPID,}_Station _16,_Lausanne _1015, Switzerland.Tel.:+41216930878;fax:+41216930885.

E-mailaddresses:shady.attia@epﬂ.ch(S.Attia),Mohamed.hassan@tkk.ﬁ (M.Hamdy),liamobrien@carleton.ca(W.O’Brien),Salvatore.carlucci@polimi.it (S.Carlucci).

andabsolute,thenumberandcomplexityofenergyuse-reducing measures,implementedindesign,andtendtoincrease[3,4].The buildingperformance objectiveshaveraisedthebarof building performance,andwillchangethewaybuildingsaredesignedand operated.Thismeansthatevaluatingdifferentdesignoptionsis becomingmore arduous thaneverbefore. Thebuilding geome-try,envelopeandmanybuildingsystemsinteract,thusrequiring optimizing thecombination ofthebuildingand systems rather thanmerelythesystemsonanindividuallevel[5].Onepromising solutionistouseautomatedmathematicalbuildingperformance optimization(BPO)pairedwithbuildingperformancesimulation (BPS)asameanstoevaluatingmanydifferentdesignoptionsand obtaintheoptimalornearoptimal(e.g.,lowestlife-cyclecost, low-estcapitalcost,andhighestthermalcomfort)whileachievingﬁxed objectives(e.g.,netzeroenergy)[6–10].

Despite optimisation’s potential in NZEB design, it largely remainsaresearchtoolandhasyettoemergeincommonindustry practice.Asthispaperreports,majorobstaclestoBPOinindustry includelackofappropriatetools,lackofresources(time,expertise), andtherequirementthattheproblembeverywelldeﬁned(e.g., constraints,objectivefunction,and ﬁnitelistofdesign options). Theobjectiveofthispaperistodocumentthecurrent state-of-the-artin terms of NZEBoptimization toolsand practice.With

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thisinformationdisseminated,itisanticipatedthatsoftware deve-lopers will be betterinformed of the needsof building design processionals.

Majorcomponentsofthepaperincludealiteraturereviewof morethan150publicationsonBPOandexistingoptimizationtools, followedbytheresultsofaninterviewthatwasusedtogainan understandingof how peoplecurrently use optimizationtools, whichtoolstheyuse,themajorlimitationstheyhaveencountered, andtheirvisionforthefutureofoptimization.Aqualitativestudy design was employed, using semi-structured interviews. Opti-mizationexpertsworkinginacademiaorpracticewererecruited. Expertswereidentifiedasresearcherorprofessionalwhohasat leastthreeormorepublicationsinthefield ofBPO.The partici-pantswereidentifiedfromtheIBPSAInternationalandRegional ConferenceProceedingsbetween1995and2010[11].Asampling frameworkwasdevelopedtoinclude expertsin thestudyfrom Asia,Europeand North America. These groups representedthe rangeofpossibleoptimizationusers,fromresearchersand design-ersconsideringoptimizationinthedesignofnetzeroorhighenergy performancebuildings.Alistofpotentialoptimizationexpertswas created(40potentialexperts)andcirculatedbetweentheIEATask 40Subtaskmembers[3].Everyinterviewedexpertwasaskedto revisethelistandaddanypotentialcandidatetobeinterviewed. Recruitmentcontinueduntilexpertsfromdifferentcountrieshad beenrepresentedandthematicsaturationhadbeenattainedforthe sampleasawhole.Anadditionalgroupofexpertshadbeeninvited duringIBPSA2011ConferenceinSydney.Intotal,28expertswere interviewedbetweenJanuaryandNovember2011.

Theinterviewquestionswereformulatedbytheauthorsand classiﬁedunderﬁvecategoriesnamely,background,methodology, output,integrationin design and shortcomings and needs. The questionnaireaimedtoprobetheuser’sexperiencewith compu-tationaloptimizationtoolsandtechniquesforthedesignofNZEBs. Priortointerviewingtheexperts,theauthorssetupapilotstudyto testsandimprovethequestionnairereliabilityandinternalvalidity. Commentsandsuggestionswererequestedfrompeerreviewers. Reviewerswereaskedtorevisethequestionnaireandprovide crit-icalfeedbackinordertooptimizetheclarityandrelevanceofthe questionnaire.

Thescopeof thestudy is limited tonZEBs,NZEBs and high energyperformancebuildings.Thosebuildingtypesareemerging asaquantiﬁabledesignconceptandpromisingsolutionto minimiz-ingtheenvironmentalimpactofbuildingssector.Thesebuildings, whichminimizeenergyconsumptionandoptimallyuserenewable resources,bothpassivelyandactivelyareusuallydeﬁnedasthose whichexportasmuchenergyastheyimport,overthecourseofa year(alsoknownasnetzerositeenergybyTorcellinietal.[12].The term‘netzero’isusedforidentifyingthosebuildingsconnectedto thegrid.Thegridisusedbothasanidealsourceandanideal stor-agemediumandenergylossesarenottakenintoduringtheenergy supplyfromthegridtothebuilding,andtheenergyfeedingfrom thebuildingintothegrid.Theissuesofmodelling,designand opti-mizationofsuchbuildingsarebeingaddressedbySubtaskB(STB) oftheIEASHCTask40/ECBCSAnnex52[1].

Key resultsoftheinterview indicatethat optimizationtools thatdoexistareprimarilycateredtoresearch,andconsequently, theydonotreﬂecttheneedsofindustry(fastturn-around,high returnoninvestedtime,easeofuse,shallowlearningcurve, user-friendlyinterfaces).Somefuturedirectionsanticipatedordesired bythosewhoweresurveyedfastercomputing(e.g.,cloud comput-ingandreal-timefeedbackofresults),improvedvisualizationof results,improvedmethodologies(e.g.,automatederror-checking, validation,anduncertaintyanalysis)andstandardizedcostsand performancedatabases.

Thispaperisorganizedintosixsections.Theﬁrstsection iden-tiﬁestheresearchproblemwithintheBPOcommunity.Thesecond

section isa literature review thatdefines thesimulation based BPOandillustratedvariousrelatedstudies,methodsandtoolsto supportit.Theliteraturereviewformsthebasisfortheinterview questions.Theinterviewresultsandanalysisarediscussedin Sec-tions3and4.Thefinaltwosectionsarediscussingtheinterview findingsandprovidingfeedbacktotooldevelopersandtothe archi-tectural,engineeringandconstructioncommunities.

2. Literaturereview

Thissectionpresentsthestate-of-the-artwithrespectto build-ingdesignoptimizationtoolsandoptimizationalgorithmscoupled tobuildingsimulationtools. Thecontentisintended toaidthe readerinbetterunderstandingareasofactiveresearchin build-ingoptimizationaswellastoolsandmethodscommonlyusedby researchersandindustry.

2.1. WhatisBPO?

Automatedbuildingperformanceoptimizationisaprocessthat aimsattheselectionoftheoptimalsolutionsfromasetofavailable alternativesforagivendesignorcontrolproblem,accordingtoaset ofperformancecriteria.Suchcriteriaareexpressedas mathemat-icalfunctions,calledobjectivefunctions.Automatedoptimization isacombinationofdifferenttypesofoptimizationalgorithms, set-tingeachalgorithmtooptimizeoneorvariousdesignfunctions. Theoptimizationobjectivesaretoidentifythecostorenergyor environmentalimpacts.

Therefore,anobjectivefunctionisdeﬁnedasamathematical functionsubjectedtooptimization.Optimizationisaprocessthat searches for theoptimalsolution withrespectto theobjective functionstobemaximized orminimized, possiblysubjected to someconstraintsofthedependentvariables.Iftheconstraintsare notspeciﬁed,theproblemisdenotedunconstrainedoptimization problem.Aconstraintlimitstheproblemspacetoasubsetof ele-ments[13].Iftheoptimizationproblemaimsatminimizingasingle objectivefunction,itiscalledsingleobjectiveoptimization prob-lem,otherwiseiftheobjectivefunctionsaremorethanone,itis calledmultiobjectiveoptimizationproblem.

Visualizationtechniquesareessentialtofacilitatetheextraction ofrelevant informationregarding performancetrade-offs, prop-agationofuncertaintiesandsensitivityanalysis.Byallowingfor visualizationduringtheoptimizationprocess,itispossibleforthe designertointeractandinformtheoptimizationprocess.

2.2. BriefhistoryofBPO

Automatedoptimizationhasbecomeincreasingly popularin awidevarietyofapplicationdomains,asreflectsabookentirely devotedtothistopic[14].Inthelate1980s,alargegroupof tech-nologicallysavvyengineering,mathematicsandscientificgroups tackled the application of automated optimization in the field AECindustryaimingtooptimizebuildingdesign andoperation. By the end of 1990s decade, many scientific groups that have well-used BPS made a transition and coupledtheir simulation worktomathematicaloptimizationmodels.Through the2000s, thedevelopmentofmathematicalandalgorithmictechniquesand theadvancementofBPStoolsgave waytoBPOtoolsthatcould solvemultiobjectiveoptimizationproblemsofadesign. Mechan-icalandstructuralengineersworkingoncomplexbuildingshave beenamongtheearlyadoptersofBPOtechniques,butarchitects and other engineers now start using these techniques as well. Today,thereis astrongtrendtowardspopulation-basedsearch algorithmssuchasevolutionaryalgorithmsandparticleswarms. Thesealgorithmshavebeenproventobeverysuccessfulin opti-mizingoneormanyperformancecriteriawhilehandlingsearch

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constraintsforlargedesignproblems[15–17].Ithasnowbecome commonpracticeforpopulationsofbuildingsimulationstobe car-riedoutsimultaneouslyonmulti-coreprocessorsanddistributed computingtogreatlyreducethetimeneededforanoptimization study(GenOpt [18],modeFrontier[19],andPhoenixIntegration

[20]).Researchershavefoundsuccessincombiningdeterministic searchesandpopulation-basedsearchestoimprovesearch reso-lutionandthereproducibilityofoptimalsolutionsetsinbuilding designproblems.

2.3. ImportanceofBPO

Inthearchitectural,engineeringandconstruction(AEC) indus-trythereisagrowingresearchtrendforautomatedoptimization approachestobeusedtomapoutandﬁndpathwaystobuildings designswithdesirablequalities,beitaesthetics,geometry, struc-ture,comfort,energy conservationoreconomic features, rather thanfocusingononeparticularoutcome.Althoughoptimization studiesaremostcommonlyperformedintheearly-designstage, wherethemajorityofdesigndecisionshaveyettobemade, opti-mizationapproachescanbeequallyusefulinthelate-designstages forselectingandﬁne-tuningcontrolstrategiesandHVACdesign andduringbuildingoperationstobestselectbuildingcontrolbased onmodel-predictivecontrolstrategies[21–24].Themost appropri-atesearchalgorithmsandmodellingapproachesvarydependingon theapplicationarea,butthesuitableapplicationareafor optimiza-tionmethodologiesrelatedtobuildingdesignandcontrolisvast andconstantlyevolving.

Moreover,theuseofoptimizationasameansofprovidinginput toenergypolicy,incentivemeasuresisoneofitsmostimportant usagesintherecentyears.Forexample,usingthebuildingenergy optimizationmodel(BEopt)developedbythenationalrenewable energy laboratory(NREL) to evaluatethe energy and cost sav-ingspotentialfromconstructingmoreefficientnewhomes and netzero-energyhomesintheUSA[25].Alsothisincludesthecall oftheEuropeanCommissionforimplementingamethodologyto calculatecost-optimal levelsin theEPBD framework. European MemberStatesarerequiredtodefinecost-optimallevelsof mini-mumenergyperformanceaccordingtotheirspecificities[26].

2.4. CombinationofBPOandsimulation

Inevitably,optimizationiscoupledtoBPStools.BPStoolsare essentialintheprocessofbuildingdesignaimingtoassesstheir energyperformance,environmentalimpacts,costs,etc.[27,28].A numberofenergysimulationenginesexistandareoftenusedin differentstageofthedesignprocessofabuilding[29,30].Outof the406BPStoollistedontheDOEwebsitein2012,lessthan19 toolsareallowingBPOasshowninTable1andFig.1[31–33].

Whendesignersdecidetoimprovethebuildingperformance, theyusually make estimation for various values of thedesign variablestobemodifiedinthebuildingenvelope,theheating venti-latingandair-conditioning(HVAC)systemandthetypesofenergy generationandrunthesimulationmanytimes.Then,designerswill trytofindtheeffectofthedesignchangesonthesimulationresults andtoconcludearelationbetweenthosevariablesandthe objec-tivesofthesimulation.Thisisaninefficientprocedureintimeand labour.Besides,therelationbetweenthesimulationvariablesand theobjectivesmaynotbesimplyunderstood,especiallywhenthere aremanyparameterstobestudied,andpossiblyduetothe non-linearityoftheproblem.Thereforeabetterdesignisnotalways guaranteed.Toovercomesuchdifficulties,automatedsimulation basedBPOsearchtechniquesareapplied.Progressionsinbuilding simulationtooldevelopmentandincouplingcomplimentaryBPS toolsatrun-timeexpanddomainswhereBPSoptimizationstudies canoccur.

In order to automate and make more efﬁcient the testing andcomparisonofseveraldesignbuildingvariants,anumberof researchershavecoupledenergysimulationtoolswith optimiza-tiontechniquesthroughself-producedtools,commonlybasedon MATLABTM_[34]_,_or_other_dedicated_software_[35]_.

2.5. Optimizationdesignvariables

Themostcommondesign variablesinBPOstudiesareeither energyrelatedoreconomicrelated.Multipleobjectivescan simul-taneouslybeconsideredthroughweightingstrategiesorbyusing amulti-objectiveoptimizationalgorithmswhichpreserves trade-offsbetweentwoormoreconflictingsearchobjectives[36].Before conductinganoptimizationsearch,firstthedesignermust iden-tifying which input design variables shouldbe includedin the optimizationsearch.Designerscanperformasensitivityanalysis toidentifywhichinputshavethelargestimpactonanobjective. Analternativeistorefertopreviousresearchtoaidin identify-inginfluentialinputvariables.Intherecentyears,severalstudies appliedBPOtechniquesinordertooptimizeaspecificaspectofthe buildingdesignoroperation.Alistfollowsdisaggregatedbythe objectiveoftheoptimization:

•Buildinglayoutandform[37–41].

•Geometry,positionanddensityoffenestration[42]. •Buildingenvelopeandfabricconstructions[15,43–51].

•Daylightingperformance[52,53]andautomatedcontrolofsolar shadings[54,55].

•Naturalventilationstrategies[56,57].

•Shapeandfunctionalstructureofbuildingsaswellasheatsource utilization[58].

•Heating,ventilating,andair-conditioning(HVAC)systemssizing

[59–63].

•HVACsystemcontrolparametersand/orstrategy[64–68]. •Thermalcomfort[69–75].

•HVACsystemconﬁgurationsynthesis[76,77].

•Managingofenergystorage[78,79]andautomatedmodel cali-bration[80,81]

•SimultaneousoptimizationofbuildingenvelopeandHVAC ele-ments[7,15,16,65,82–90].

•Simultaneous optimization of building construction, HVAC-systemsize,andsystemsupervisorycontrol[91–93].

•Simultaneousoptimizationofbuildingconstruction,HVAC ele-mentsandenergysupplysystemincludingRES[94–98].

AlsoseveralPhDworkapproachedBPOincludingtheworkof Caldas[99],Nielsen [100], Wetter [101], Wang[102], Pedersen

[103],Verbeeck[104],Choudhary[105]andHopfe[106].

However,therearesigniﬁcantdisparitiesbetweentheabove BPOapplications.Someofthemapplymulti-objectiveoptimization whiletheothersdosingleobjectiveones.Theimplemented opti-mizationalgorithmsrangefromenumerativetostochasticones. Thesizeandcomplexityoftheaddressedsolutionspacesarequite different.SomestudiesuseddetailedBPStoolswhileothersused simpliﬁedones.Inordertoreducethesimulationtime,three strate-giesarecommon:

•Customsimpliﬁedthermalmodelaredevelopedandusedinstead ofexisteddetailedBPSsoftware[77,100,107–109].

•DetailedBPStoolsareusedforsimulatinggeometrically simpli-fiedmodels: e.g.asinglezonemodel isusedforrepresenting onefloorsinglefamilyhouse[85],atwozonesmodelfora two-storyhouse[17],asimplifiedmodelforrepresentinga200m2

house[110],andtworepresentativezonesareusedtoevaluate thethermalperformanceofoneﬂoorinofﬁcebuilding[111].

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Table1

ClassiﬁcationofBPOtools.

Simulationbasedoptimization Optimizationpackages Tailormade-programming

Public TRNOPT(2004)

BeOpt(2005) OptiMaison(2005) OptiPlus(2006)

Private/commercial ARDOT(2002) MATLABoptimizationtoolbox(1990) Topgui(1990) C++

Polysun(2006) Phoenixintegration(1995) GenOpt2001 Cygwin

GENEARCH(2008) GAlib(1995) ParadisoEO2003 Java

Lightsolve(2008) modeFrontier(1999) ThermalOpt2011 R

ParaGen(2011) Homer(2000) VisualStudio

ZEBO(2012) DER-CAM(2000)

•DetailedBPStoolsareusedforsimulatingamodelonlyfora rep-resentativeperiod:e.g.,fewdaysareusedasaweathersamples [112,113]and6monthsisusedasarepresentativeperiod[111]

forthewholeyearweatherconditions(temperature,humidity, windspeedandsolarradiation).

2.6. BPOobjectives(single-objectiveandmulti-objective functions)

Generallyspeaking,optimizationcanbeeithersingle-objective ormulti-objectiveaccordingtothenumberofobjectivefunctions thatdefinetheoptimizationproblem.Inthecaseofoptimizinga single-objectivefunction,anoptimumsolutionoftheproblemis eitheritsglobalmaximumorminimum,dependingonthe pur-pose.Ingeneral,itisaconventioninmathematicaloptimization, thatoptimizationproblemsarecommonlydefinedas minimiza-tionsofthequantity,instead,ifanoptimizationproblemconsistsin themaximizationofanobjectivefunction,itissufficienttominimize itsopposite[114].Inmanyrealproblems,itisrequiredtosatisfy simultaneouslymorethanoneobjectivefunction.Suchproblems aredenotedmulti-objectiveoptimizationproblems.

In multi-objective optimization problems, a single solution couldnotbeabletominimize(ormaximize)simultaneouslyeach objectivefunction.Rather,whensearchingforsolutions,onecomes tolimitvariantssuchthat,afurtherimprovementtowardsthe min-imumvalueofoneoftheobjectivefunctioncausesaworsening oftheclosenesstominimumoftheothers.Therefore,theaimof amulti-objective optimizationproblemconsistsin ﬁndingsuch variantsandpossiblyinquantifyingthetrade-offinsatisfyingthe

individualobjectivefunctions.Theroleoftheoptimization algo-rithmistoidentifythesolutionswhichlieonthetrade-offcurve, knownastheParetoFrontier,whichisinwords,asetofoptimal solutionsplottedintheformofacurve(namedafterthe Italian-Frencheconomist,VilfredoPareto,seeFig.10.5).Thesesolutionsall havethecharacteristicthatnoneoftheobjectivescanbeimproved withoutprejudicinganother.ThevariantsofaParetoFrontierare deﬁnedaselementsthatarebetterthanothersinrelationto,at least,oneobjectivefunctionandsimultaneouslynotworse con-cerningallotherobjectivefunctions.

2.7. AlgorithmsusedinBPO

Optimizationofabuildingasawholeisacomplexproblemdue totheamountofdesignvariablesaswellasthediscrete,non-linear, andhighlyconstrainedcharacteristics.Thepopularoptimization methods for solving multi-objective optimizationproblems are generally classiﬁedintothreecategories:(1) enumerative algo-rithms,(2)deterministicalgorithms,and(3)stochasticalgorithms. Theenumerativemethodssearchinadiscretespace.They eval-uateallthesolutionsandchoosethebest.Thesealgorithmsare computationallyexpensiveandconsequentlytheyarenotsuitable forexploringwidesolutionspaces.Twotypesofmethodscanbe found:(1)gradientand(2)gradient-freedeterministic.The gra-dientonesusethegradientoftheevaluationfunctionseitherby goingin thedirectionwhere thegradientis thesmallestorby searchingsolutionsthathaveagradientvectorequaltozero.The gradient-freeonessuchasHooke–Jeevesdirectsearch[115], con-structsasequenceofiteratesthatconvergetoastationarypointif

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thecostfunctionissmoothandcoercive.Emmerichetal.[116,117]

usedtheHooke–Jeevealgorithmisusedtominimizetheenergy consumptionconsideringdifferentbuildingscenariosand charac-teristics.Agradient-freesequentialquadraticprogramming(SQP) ﬁlteralgorithmisproposedandtestinPedersen’sPhDwork[103]. Thealgorithmcanconvergefastandinastablemanner,aslongas therearenoactivedomainconstraints.

Generally,thedeterministicalgorithms needthat the evalu-ationfunctionshaveparticularmathematicalpropertieslikethe continuityandthederivability[15,82].Therefore,theyarenotthe bestchoiceforhandlingdiscontinuousbuildingandHVAC prob-lemswithhighlyconstrainedcharacteristicsandmulti-objective functions.Ontheotherhand,theadvantageofthestochastic algo-rithmsisthattheydonothavemuchmathematicalrequirements forsolvingtheoptimizationproblem[118].Examplesofstochastic algorithmsthatare designedtodeal withhighly complex opti-mizationproblemsare [119]:annealing [120–122], tabusearch

[123],antcolony [124], particleswarm[125]and genetic algo-rithms[126–128].

Stochasticelementwasaddedtopatternsearchalgorithmfor optimizingthetopologicaldesignofthebracingsystemfora free-formbuilding[129].Antcolonyoptimizationalgorithm(ACO)was usedtosearchforatrade-offbetweenlightintake,thermal per-formance,view,andcostfor apanelledbuildingenvelopefor a mediacentrein Paris[130].Astrengthmulti-objective particle-swarmoptimization(S-MOPSO)wasusedfortheoptimizationof aheating,ventilation,andairconditioning(HVAC)systeminan ofﬁcebuilding[131].

Insteadof theabove algorithms, thelast 10 years has seen anincreasinginterestinusinggeneticalgorithms(GAs)for opti-mizationof buildingand HVACsystems. TheGAs are themost efficientstochasticalgorithmswhentheoptimizationproblemis notsmoothorwhenthecostfunctionisnoisy[132,133].TheGAs considermany pointsinthesearchspacesimultaneously,not a singlepoint,thus theyhavea reduced chanceofconvergingto localminimum,inwhichotheralgorithmsmayendup[107].The GAswiththeParetoconceptareusedwidelyinenergyand build-ingsstudies[7,16,40,41,47,48,92,94,102,110,134–136].According to the studies of Zitzler [137] and Deb [128], the elitist non-dominatedsorting geneticalgorithm(NSGA-II)seemstobethe mostefficientGAs.TheNSGA-IIisimplementedtofindtrade-off relations betweenenergy consumption and investment cost or thermalcomfortlevelofbuildings[70–72,86,106,111,138,139].The NSGA-II[128]couldbeoneofthemostsuitableoptimization algo-rithmstohandlemulti-objectivemultivariatebuildingandHVAC designproblemswithdiscrete,non-linear,andhighlyconstrained characteristics.Howeverbecauseitsstochasticbehaviour,itcould occasionallyfailtogetclosetothepareto-optimalfront, partic-ularlyiflownumberofevaluationsisimplemented[86,87].The highnumberofiterationsischosentoavoidtheearlybreakdown oftheoptimization[106].Sincebuildingsimulationisoftenvery time-consuming,alargenumberofiterationscouldnotbepractical. Deterministicoptimizationphasesandarchivestrategiesareadded totheoriginalNSGA-IIinordertoperformrapidoptimization– usingalownumberofsimulationruns–and/ortoguarantee opti-malorclose-tooptimalsolutionsetforbuildingdesignproblems

[17,87,98].The proposedalgorithms/approaches (PRGA, GARF, PRGARF,andaNSGA-II)reducetherandombehaviourofthe origi-nalNSGA-IIenhancingtherepeatabilityoftheoptimizationresults.

2.8. ToolsofBPO

AsshowninTable1,BPOtoolscanbeclassiﬁedintotwomain group’sstand-aloneoptimizationpackagesandsimulationbased optimizationtools.The listof stand-aloneoptimizationtools is notverylong,howeverwechosetopresentthemostfrequently

mentioned tools in literature namely GenOpt®_, _MATLAB®_, modeFrontier® _and _Topgui®_. _However, _in _the _past ₁₀ _years, severaladvanceshavebeenmadetodevelopbuildingsimulation toolsthat are drivenby feedbackfrom performance objectives. Largelythesetoolsaredirectlytowardsindustrytodramatically decreasetheenergyfootprintofnewbuildings.Considerthemost mentioned two tools in literature that attempt to merge both optimization and simulation techniques developed at national renewableenergylaboratories(NREL):BeOptTM_and_Opt-E-PlusTM_.

2.8.1. GenOpt®

GenOpt® _is _stand-alone _optimization _software _developed _at LawrenceBerkeleyNationalLaboratory(LBNL).GenOptisageneric optimizationprogramme that canbeused withanysimulation programmethathastext-basedinputandoutput,suchas Energy-Plus,DOE-2,IDA-ICE,SPARK,BLAST,TRNSYS,oranyuser-written code[140].Itissuitabletobecoupledwithanytext-based simula-tionprogramme.Thistoolisabletoaccessalibraryofdifferent optimizationalgorithms, and can useeither continuous or dis-cretevariables. Themodularity, ﬂexibility,and ability toselect froma range of optimizationstrategies make GenOpt a robust platform,butitsvisualizationcapabilitiesarelimited.Thetoolis aimedto solveproblems where the objective functionis com-putationallyexpensiveanditsderivativesarenotavailableordo notexist,thusitisnotsuitableforlinearprogrammingproblems, quadraticprogrammingproblemsandproblemswherethe gradi-entoftheobjectivefunctionisavailable.Theindependentvariables can be continuous, discrete or both. Constraints on dependent variables can be implemented using penalty or barrier func-tions.GenOpt® _provides_{multidimensional}_and_{one-dimensional} optimization algorithms. However, its library doesnot include multi-objectivealgorithms.

Thealgorithmsformultidimensionaloptimizationare:(i) gen-eralized pattern search methods for continuous independent variables(thecoordinatesearchalgorithmandtheHooke–Jeeves algorithm),whichcanalsoberunusingmultiplestartingpoints, (ii)discreteArmijogradientforcontinuousindependentvariable, (iii)particleswarmoptimizationalgorithmsforcontinuousand/or discrete independent variables, which can beused in the ver-sionswithinertiaweightorwithconstrictioncoefficientandwith amodificationthatsetthecontinuousindependentvariableson afixedmeshinordertoreducecomputationaltime,(iv)hybrid generalized patternsearchalgorithm withparticleswarm algo-rithmsforcontinuousor/anddiscreteindependentvariables,and (v)simplexalgorithm ofNelder andMeadfor continuous inde-pendentvariables[141,142]. Ontheotherhand,thealgorithms for one-dimensional optimization are: (vi) the golden section intervaldivision and(vii)theFibonaccidivision. GenOpt® auto-maticallyallowsparallelcomputingifthecomputerhasmultiple CPUs,significantlyreducingcomputationaltime[143].The modu-larity,flexibilityandwideavailabilityofoptimizationtechniques makeGenOpt®_a_robust_optimization_environment,_but_its post-processingcapabilitiesarelimited[47].

IntheﬁeldofBPO,GenOpthasbeenusedbyseveralresearchers includingCoffeyetal.[144,145],CongradacandKulic[107],Corbin etal.[22],Djuricetal.[146,147],Jacobetal.[148],Hasanetal.[85], Kummert[149],Magnieretal.[150,151],Palonenetal.[86],Park etal.[152],Henzeetal.[153],Stephanetal.[57],WetterandWright

[15]andWrightandFarmani[91].

2.8.2. MATLAB

Forlesssimulationeffortsandfeasibleoptimizationresults,it isessentialtodevelopthelinkbetweenexistentbuilding simula-tiontoolsandtrustedoptimizationtools.Inenvironmentaldesign ofbuildings,sincethenumberofdesignvariablesisusuallylarge

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andthetruenatureofsolutionspace(linearornon-linear) can-notbeknown,optimizationtoolhastoprovideaccesstodifferent typesofalgorithmstosuitproblemneeds.Thisaspectisprovided intoMATLABwhichistrustedtoolcomprisesalotofoptimization solversabletodealwithdifferenttypesofoptimizationproblem. Additionally,withthisapproach,theusercanutilizeallMATLAB functionswhichprovidesigniﬁcanttoolstoattainandanalysisthe optimalresults

MATLABOptimizationToolboxTM _provides_a _variety_of

algo-rithms for optimization problems. These algorithms solve con-strained and unconstrained continuous and discrete problems. MATLAB includes functions for linear programming, quadratic programming,binaryintegerprogramming,nonlinear optimiza-tion,nonlinearleastsquares,systemsofnonlinearequations,and multi-objectiveoptimization.Thisallowsﬁndingoptimalsolutions, performingtrade-offanalyses,balancingmultiplebuildingdesign alternatives,andincorporatingoptimizationmethods into algo-rithmsandmodels[34].

IntheﬁeldofBPO,MATLABhasbeenusedbyseveralresearchers includingBucking[9],Choudhary[105],Coffey et al.[144,145], Jacobetal.[148],Hasanetal.[85],Hamdyetal.[87],Henzeetal.

[153],Kummert[149],Parketal.[152],Sheaetal.[130],Wetter

[101],WrightandFarmani[91].

2.8.3. modeFRONTIER

modeFRONTIERisamultidisciplinaryandmulti-objective soft-warethatallowscomplexalgorithmstospottheoptimalresults, evenconﬂictingwitheachotherorbelongingtodifferentﬁelds. Thetoolbecoupledtodifferentothersoftwarepackagesindifferent input/outputinterchangeformatsincluding:EnergyPlus,ESP-r Flu-ent,andMATLAB.Oncedatahavebeenobtained,theusercanturn totheextensivepost-processingfeaturestoanalyzetheresults.The softwareofferswide-rangingtoolbox,allowingtheusertoperform sophisticatedstatisticalanalysisanddatavisualization.

ThetoolhasbeenusedbyXing[154]ﬁndthebestinsulation strategytominimizethespaceconditioningloadofanofﬁce build-ingwhilekeepingtheinsulationusageatminimum.Alsothetool hasbeenusedbytheunitofbuildingphysicsandsystems, Eind-hovenUniversityofTechnologyintheNetherlands,includingthe workofHoesetal.[138]andLoonenetal.[139].

2.8.4. Topgui

TopguiisaMATLABTM_graphical_user_interface_(GUI)_programme

originally developed to be coupled with ﬁnite element analy-sis models for executing topology optimization. In the current version,it providesseveralsingle-objective and multi-objective optimization techniques: Hooke–Jeeves algorithm, generalized patternsearchmethods,particleswarmoptimizationalgorithms, evolutionarystrategy, non-dominated sorting geneticalgorithm II(NSGA-II),s-metricselectionevolutionarymulti-objective opti-mizationalgorithm(SMS-EMOA).

IntheﬁeldofBPOandaccordingtotheliteraturereview,Topgui hasbeenusedmainlybyHopfe[106]andEmmerichetal.[116,117]

aimingtoevaluateoptimizationmethodologiesforfuture integra-tioninBPStools.

2.8.5. Opt-E-PlusTM

Opt-E-Plusisatooldeveloped byNRELthatusesEnergyPlus simulationengine.Opt-E-Plusutilizesvarioussearchroutinesto identify optimalbuildings designs for energy usage [155]. The frameworkconsistsofacollectionofEnergyPlusinputandoutput files,systemdirectories,andcomputerroutinesthatuseanXML datamodeltotransferinformationamongthevariouscomponents. Theuserisabletomodifyparametersina.xmlfile,ratherthan directlymodifyingtheEnergyPlusinputfiles.Thisapplication inte-grateswithmultipledatasources,ismodulartoallowdistributed

programming,andsupportsselectionofautomationand optimiza-tionstrategies.Althoughthisisnotastand-aloneoptimizationtool, itisdevelopedtoguidetheuser,throughthecomparisonof vari-ousdesignoptions,towardsthemosteconomicalenergysavings. Thestructureoftheprogrammeismodulartoallowdistributed programming[155].Visualizationofthetradespacehoweveris limited, and itdoesnot supportmultidisciplinary optimization. AlsotheprogrammeisrestrictedtoNorthAmericancontext. Opt-E-PlushasbeenusedbyNRELresearchersandothersincludingthe workofHerrmannetal.[156]andLongetal.[157].

2.8.6. BEoptTM

BEoptTM_is_a_tool_developed_by_NREL_and_is_designed_to_identify

optimalbuildingdesignvariantsonthepathtonetzeroenergy target. The software allows the user to select discrete options for various building variables regarding building envelope and HVAC systems and calculates energy savings withrespectto a user-deﬁnedreferencecaseoraclimate-speciﬁcBuilding Amer-icaBenchmark[158,159].Regardingenergysimulation,BEoptTM

canuseassimulationengineeitherDOE-2[160]orTRNSYS[161]

andtheoptimizationisexecutedbyasequentialsearchtechnique inordertoﬁndthemostcosteffectivecombinationofenergy efﬁ-cientmeasuresandphotovoltaicsystems[8].Thesoftwarerapidly providestheuseradesignspace(orproblemspace);howeverthe proposeddesignspaceandtheselectableobjectivefunctionsare limited.AlsotheprogrammeisrestrictedtoNorthAmerican con-text.

Constructionvariables suchaswindow andwalltypes were usedasinputsforaDOE-2.1energymodelandTRNSYSwasused toimplementsolarmodules.Morerecently,theenergymodelhas beenupdatetouseEnergyPlusforbothbuildingandsolar compo-nentsimulations.AnobjectiveofBEopt’sapproachisnottofocuson onefinaloptimalsolution,butonpathwaystooptimaldesignssuch thattheusercanselectdesignswhichbestsuittheirfinancing avail-ableforthehousingproject.IntegrationwithSketchUp,acomputer aideddesigntools,greatlysimplifythecreationofbuildingmodels usedforbuildingsimulation.Opt-E-Plusisacommercialbuilding optimizationtoolwhichusessequentialsearchesandEnergyPlus forbuildingsimulations.Opt-E-PlusalsointegrateswithSketchUp tocreatebuildingmodelsusedforoptimizationstudies.The appli-cation includesa graphical userinterface(GUI)that allows the usertoselectfrom arange of predefinedand discrete building alternativestobeusedintheoptimizationprocess.BEoptallows theusertorapidlygenerateandvisualizethedesignspacethrough abrowser,butitsflexibilityislimitedasaresultofhaving prede-finedbuildingalternativesanditsinabilitytoconsiderawiderange ofobjectivefunctions.

BEopthasbeenusedbyNRELresearchersandothersincluding theworkofAndersonetal.[162],Givler[163]andPollyetal.[164].

3. Interviewresultsandanalysis

TheprevioussectionwasaliteraturereviewthatdefinedBPO andillustrateditshistory,methods,characteristicandtoolsto sup-portit. Thissectionpresentssomeoftheinterview resultsthat interviewedoptimizationexpertsin2011.Eachinterviewincluded 25questionsavailableinthefinalstudyreport[165].Forthispaper, representativequestionsthatreflectthemostimportantfindings areselected.Thecompleteresultsarepresentedandcanbefoundin thefinalstudyreport[165].Priortoanalysingtheinterviewresults, itisimportanttoquestionthestatisticalsignificanceofthe inter-viewsample.Infact,28participantswereinterviewedfromalistof 40potentialusers.ThelistwasdevelopedbytheIEAtask40 mem-bersandtheinterviewedexpertsthemselves.Thustheinterview sampleishighlyrepresentativeofresearchers.

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Fig.2. Fieldofdisciplineofinterviewedexperts.

3.1. Interviewee’sbackground

Whatisyourmajorﬁeldofdiscipline(architecture,engineering, computerscienceother)?

28expertswereinterviewedwhere26hadtheirbackground inengineering,2hadtheirbackgroundinphysics,onein architec-tureandoneincomputerscience(Fig.2).Amongthe28experts26 identifiedthemselvesasresearchersand2identifiedthemselves aspractitioners(Fig.2).Theaffiliationoftheinterviewedexperts showsthattheyaremostlylocatedinuniversitiesorresearchlabs intheNorthernHemisphere.Themajorityofintervieweesworkin theUnitedStates(29%),theUK(18%),Canada(14%),Finland(10%), theNetherlands(7%),Germany(3%),Switzerland(3%)andJapan (3%).

Howmanyprojectsorcasestudieshaveyouperformedandhow longdoeseachprojectorcasestudytake?

Inaverage40%ofallinterviewees(11)conductedbetween5 and10optimizationcasesorprojects,32%(9)conductedlessthan5 casesorprojectsand11%(3)conductedbetween10and15 optimi-sationswhileonly14%(4)conductedmorethan15optimisations. Mostintervieweesmentionedthattheystartwiththemodel devel-opmentandcalibrationfollowedbylinkingthesimulationtoolto theoptimizationtool,andthenruntheoptimization.Fig.3shows thetime foreachcase orprojects.Intervieweesmentioned that thedevelopmentandcalibrationofthesimulationmodelsareone ofthetimeconsumingsteps,requiringinaverage2–3weeksof work.However,therunningtimeoftheoptimizationsimulations isthemosttimeconsumingprocessanddependingonthemodel resolutionsthetimerequiredforeverycasevariessigniﬁcantly.

Whatkindoftoolsdoyouuseforoptimization(MATLAB,GENOPT, others)?Towhichsimulationtooldoyoucoupleit?

Fig.4revealsthatMATLABtoolboxandGenOptarethemost usedoptimisationstools.Theleftﬁgureindicatesthatthemost usedsimulationtoolsamongintervieweesis(9)EnegryPlus and (7)IDAICEfollowedby(5)TRNSYSand(3)Esp-r.

3.2. Optimizationmethodology

Whichbuildingtypologieshaveyouusedoptimizationforandin whichclimates?(Residential,ofﬁces,retail,institutional)

Fig.5showsthebuildingtypologies,constructiontypesand cli-mateweretheprojectswereoptimized.

Howmanyzones doyouaddress inyour modelwhen running optimisations?Andwhatkindofdesignvariablesdoyousetfor opti-mization?

64%oftheintervieweesusedmulti-zonemodelwhile36%used singlezonesmodels.Intervieweesindicated thatthepreference ofchoicebetweenthesingleandmulti-zonemodellingdepends onthemodelresolution(levelofdetail)andtheexpected inter-actionsbetweentheeachthermalzoneandthesystems.Alsothe multi-zonemodelwasusedtodifferentiatebetweenheatedand non-heatedzonesandbetweenfrequentlyandlessfrequentlyused spacesofthebuilding.

Asshown in Fig. 6, themost optimized design variables by the interviewed experts for NZEBs were systems and controls

(53%)followedbytheenvelope(50%).Theoptimizationofcontrol systemsandinparticularmodelpredictivecontrolwasconsidered asoneofthemostcomplexanddynamicdesignvariables there-fore,designoptimizationwasnecessary.Renewablesystemswere optimizedby50%oftheinterviewees.Thermalstorage,layoutand

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Fig.4.Optimizationtoolsorderbyuse(right)andsimulationtoolsorderedbyuse(left).Thelinethicknessisproportionaltothefrequencyofthepairings.

geometrywasoptimizedby25%oftheintervieweesfollowedby

internalgains(18%).11%oftheexpertsoptimizedoccupancyand 7%optimizedlocationandclimate.TheanalysisofFig.6showsthat themostoptimizeddesignvariableswherelatedesignparameters. Accordingtotheintervieweesthechoiceofthedesignvariablewas basedontheinnovationofthedesignprojectandthecomplexity ofaparticulardesignvariable.

Whatkindofobjectivesdoyousetforoptimization?

Commonoptimizationcriteriain buildingdesign arevarious costssuchas initialcapital costand annualoperating cost,and lifecyclecost, energyconsumption andrecently environmental impact.70%of allintervieweesdo multi-objectiveoptimization versus30%whodosingleobjectiveoptimisations.Regardingthe

objectives,all interviewees(28)choseenergy asthemostused optimizationobjective,while64%(18)chosecost.

Thecostobjectivesincludedthelifecyclecost,initialcost, opera-tionandmaintenancecost.Comfortfollowed(10)asthethirdmost importantobjectivewhilesomeintervieweesindicatedthatthey considercomfortasaconstraintsoIwouldn’tcallitanobjective. AsshowninFig.7carbon(5),lighting(2)andindoorairquality(1) wererankedattheend.

Whatkindofconstraintsdoyousetforoptimization?

AsshowninFig.8,therewasagreementamongmost interview-ees(22)tosetthermalcomfortasthemainconstraintfollowed by cost (18). Interviewees refer to comfort conditions deﬁned bystandards.Therewasanagreementtoconsiderconstrainsas

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Fig.6.Participants’choicesofoptimizationdesignvariables.

Fig.7. Participants’choicesofoptimizationobjectivefunctions.

primarilytodeﬁnethefeasibledomain.Alsopenaltytermsareused intheoptimizationworktobothguidetheoptimizerawayfrom infeasibleregionsandalsotoconsidertheimpactofthermal com-fortboundariesontheoptimization.Constraintsinthiscasewere boundaryorequationbased.

Under which setting you run you optimization what is your methodology?Whatkindofstoppingcriteriadoyousetfor optimiza-tion?

Theanswertothisquestiondependedontheusedalgorithm. Intervieweesindicatedthatsomealgorithmshavestopping crite-riabuiltin,othersrunforaprescribednumberofgenerationsor

Fig.8. Participants’choicesofoptimizationconstraints.

Fig.9. Participants’choicesofoptimizationstoppingcriteria.

simulations.However,asshowninFig.9mostinterviewees(17) setanumberofgenerationsasstoppingcriteriafortheir optimiza-tionwork.Somesetatimelimit(4),ornostoppingcriteriaatall(4) whilefew(3)setanumberofsimulations.

3.3. Output

DoyouhaveGUIforyourownoptimizationtool?Andwhichkind ofoutputanalysisvisualizationdidyoudousingoptimizationtools

(1–14)?

75%ofintervieweesindicatedthattheydonothavean environ-mentorpackagewithaGUIforoutputpostprocessingandanalysis visualization.Mostintervieweesareforcedtoprocessandconvert theoutputdatausingdifferentprocessingtools,suchasDView, Excell,gnuplotorwritingscriptsin MATLAB,in ordertocreate interpretableoutputresults.

Fig.10illustratesthe14mostusedoutputanalysisgraphs.22of theintervieweesusethegraphFig.10.5allowingplottingthe solu-tionspaceusingtheParetoFront.Intervieweesindicatedthatthe ParetoFrontincludemanysolutionthattheycanpickfroma vari-etyofsolutions.ThiswasfollowedbyFig.10.8(15interviewees) thatallowsthevisualizationofenergy,costorcarbonemissionsof differentsolutioncasesrepresentingthebasecaseversusthe opti-mizedcase.AlsoFig.10.4and10.6wasselectedby12interviewees tovisualizetheimpactofanyparametervariation.Ingeneral,every respondenthadhisorherowncustomvisualizationtechniques,for examplelineplots(Fig.10.2)ortimeseries(Fig.10.7)areusedfor controlsandinthecaseofcomfortscatterplots(Fig.10.2)areused.

3.4. Integrationofoptimizationwithdesignprocess

Thispartoftheinterview wasstructuredarounda seriesof openquestions inordertogetmoreinsightsontheintegration ofoptimizationtechniquesinthedesignprocess.Aselectionofthe commentsandtheirfrequencyisclassiﬁedasfollows:

Whatopportunitiesyouseeinintegratingoptimizationtechniques inNZEBdesignprocess?

AccordingtotheintervieweesBPOhavebeenapplied success-fullyinnumerousNZEBprojects.Howeverthebuildingsimulation communitystillrarelyusesoptimizationandlittleinvestmenthas beenmadetoadvanceBPO.However,intervieweesindicatedthat manyopportunitiesinintegratingsimulationbasedBPOinNZEB designandoperation.Themostmentionedopportunitiesinclude: •SupportthedecisionmakingforNZEBdesign.Theriseofsimulation hasbeendrivenbymany things,includinggovernmentpolicy

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Fig.10. The14differentoutputanalysisvisualizations(1)solutionfitness,(2)solutionsprobabilities,(3)solutionrange,(4)solutionline,(5)solutionspace(ParetoFront), (6)parametricweights,(7)timeseries,(8)solutioncomparison,(9)solutiontree(Dendrom–HaleandLong[166]),(10)lineartradeoff–HopfeandHensen[167]),(11) table,(12)dendrogram(clusteringofvariables– Bucking[9]),(13)fitnessandaveragefitness,and(14)thermalcontourplot).

thatpushesthedesignoflowenergybuildings.Atpresent,any increaseintheuseofoptimizationwillbedrivenbytheextent towhichaidsdesigndecisionmaking.Inthisrespect,oneofthe mostpowerfulformsismulti-objectoptimization,sinceitgives asetofsolutionsthatlieonthetrade-offbetweentwoormore conﬂictingdesignobjectives.Thetrade-offcanbeusedtoexplore theimpactofsayoflesscapitalinvestmentontheincreasein carbonemissions.Thiskindofinformationbeingusefulin deci-sionmakingofNZEBrequiringlittleeffortandgeneratesdifferent ideasandalternatives.

•Designinginnovativeintegrated NZEBs andthermal (andvisual)

comfortcontrolsystemsaredifficulttodesignbecausetheyinvolve complexsystemsthatinteractdynamically.Optimization algo-rithmcanhelpinfindingtheoptimalandnearoptimalsolutions regardingthedesignandsizingofpassiveandactiveenergy sys-temsandfindingthebalancebetweendemandandproduction. •Achieving cost-effective NZEBs by analyzing and synthesizing

multi-physics systems that may include passive and active facades,lightingcontrols,naturalventilation,HVAC,andstorage ofheatinthebuildingstructurecombiningadvanced technolo-giessuchasmicro-CHP,PV,PVT,solarcollectorsandmicro-wind). The complexity of such systems pose a serious challenge to designersandusingBPOisanopportunityforoptimaland cost-effectivedesign decisionduringbuildingdesign andoperation includingtheexistingbuildingstock.

•Allowoptimalsystemsschedulingthroughmodelpredictivecontrol

(MPC)takingintoaccountthedynamicsofNZEBsystemsand anticipatedfutureenergyload.Whensolvingtheoptimalcontrol problemusingMPCalgorithm,itdeterminenear-optimalcontrol settingsduringdesignandoperationandimprovetheNZEBload matchingproblem.

Howcanitbeintegratedintothedecisionmaking?Howshould optimizationbecome more practically applied during early design phases?

Therewasanagreementamongintervieweesthatpriortoany integrationefforttheremustbeﬁrstcommercialtoolsavailable

withintegratedsimulationandoptimizationthatallowseamless linkbetweenthesimulationmodelandtheoptimizationprocess. Currently,thetimeandknowledgerequiredimplementing sepa-ratesimulationmodelsandoptimizationalgorithmsislimitingthe useofBPOinpractice.However,onthelongtermtheintegration ofBPOcanbeachievedthrough:

•RequiringoptimizationasastandardactivityduringNZEBsdesign and operation. BPO can be integrated and become standard in practice.ConsequentlyBPStool willintegrate optimization techniquesandthenumberofuserswillincreasedramatically. In the coming year, I expect it to be a standard feature in NZEBs.

•Planning optimization early in the design process. BPO should be introduced in early phases of design as part of the inte-grated design process (IDP). The use of optimization should be during schematic design stages. Modelsshould be simple withsomegeometricalzoningsimpliﬁcation.Usingastandard reference building and testing all kind of technologies can helpinestablishinginitialdesignconceptsandsolutionswhich canhaveanimpactonallstakeholders.Showingresultsfrom the starting point can have a strong impact on cost, energy and thermal comfort and will allow a range of ideas and solutions.

•Informing all building stakeholders on the importance of opti-mization. Comparison studies on buildings with optimization andbuildings withoutoptimizationwillinformdesignersand clients. The optimization community should show designers that the use of optimization tools produce better results. By providing demonstration projects and real physical build-ingsbesidetheoptimizationmodelsforsimulationusers.This will raise the conﬁdence in the optimization and lead to more detailed and accurate and certain optimization models withoperationpattersand hours.Education inacademia and practice has a key in guiding professionals how to perform optimization.

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3.5. Optimizationshortcomings

Whatarethemajorpracticeobstaclesofintegratingoptimization techniquesinNZEBdesign?

Themajorobstaclesofintegratingoptimizationtechniquesin NZEBdesigncanbeclassiﬁedundertwomaincategories:(1)soft obstaclesand(2)hardobstacles.Themainfoursoftobstaclesand theirfrequencyislistedasfollows:

•LowreturnandthelackofappreciationamongtheAECindustry (19)

•Lackofstandardsystematicapproachtoperformoptimizationin mostcasesresearcherfollowmanydifferentmethodsandad-hoc approacheswithoutastructureandcategorisationinuse(16) •Requirementofhighexpertise(11)

•Lowtrustintheresults(5)

Interviewees’indicatedthatinpractice,thereisalackof aware-nessand conﬁdence ontheuseof optimization.Alsoit is very importantthatusersunderstandtheoptimizationprocess.There isalargeeducationalneedbeforeBPOgetsappliedroutinelyinthe designprocess.

Regardingthe hard or technical obstacles, theinterviewees’ commentsandtheirfrequencyislistedasfollows:

•Uncertaintyofsimulationmodelinput(27) •Longcomputationtime(24)

•Missinginformationoncost,occupancyschedules,etc.(19) •Difﬁcultyofproblemdeﬁnition(objectivesarrangementand

con-straintviolation)(12)

•Missing environments integrating and linkingsimulation and optimizationseamlessly(16)

•Low interoperability and ﬂexibility of models for exchange betweendifferentdesign,construction,simulation,cost estima-tionandoptimizationtools(11)

•LackofenvironmentwithfriendlyGUIallowingpostprocessing andvisualizationtechniques(7)

Interviewees’agreedthatcomputationtimeisverylongandthis mightwellinhibittheinitialtake-upofoptimizationinpractice. Theoptimizationprocessesalsomagniﬁestheideaof “rubbish-in-rubbish-out”sinceratherthansimulateasingledesignsolution, theerrorsorinaccuraciesinasimulationareexposedacrossawide rangeofthedesignspace.Thismayleadtoaneedforbetter educa-tionandimproveduserinterfacesforsimulation,aswellasmore workontheuncertaintyassociatedwithsimulationmodels.

Whichtoolswouldyourecommend?

10intervieweesrecommendedGenOpt,6MATLAB,4BeOpt,2 modeFrontierand1Topgui.

Whatfeatureswouldyouliketoﬁndinfuturetools?

Interviewees mentioned many ideas that contrast the hard obstaclesmentionedpreviously.However,somesigniﬁcantideas onfuturefeatureofoptimizationtoolsinclude:

•DoingoptimizationinrealtimewithinaBIMmodelandallowing adjustmentontheﬂy

•Allowingparallelcomputingtoreducecomputationtime •DevelopbetterGUIandmodelthebuildingin3D •Couplesimulationandoptimization

•Connectrealphysicalbuildingcomponentsperformanceto opti-mizationmodelsforbetterinformationoncostandoccupancy etc.

•Allowautomationofbuildingsimulationwithsomedefault tem-platesandstrategies

•Proﬁtfromthegamingindustrybydevelopinginteractive opti-mizationenvironmentsforexampletalkingtoanoraclefriend

orwizardthatguidestheoptimizationprocessforbetterinput qualityanderrordetectionanddiagnostics

4. Discussion

Fromtheinterviewresultsthreethemeswereidentiﬁed:the optimizationcontext,thelocusofoptimization,andthefactorsthat inhibittheuptakeofBPOasdecisionsupportinthedesignofNZEBs.

4.1. Summaryofmainﬁndings

For most interviewed experts evolutionary algorithms were foundasabreakthroughthatcanhelpsolvinghighlyconstrained envelope,HVACandrenewableoptimizationproblems,while con-ventionalalgorithmsjustcouldnotdoit.Simplegeneticalgorithm solvedmanydesign,operationandcontrolproblemswithrelative ease.Evolutionaryalgorithmsareadaptableandverypowerfulin findinggoodsolutions.Itisdifficulttoknowwhethertheyhave foundglobalminima,butthisisnotacriticalflawsolongastheycan measuretheimprovementintheoptimalityofasolutionagainst abasecase.Itisalsoarguedthatthenotionoftryingtofindan optimumisnonsensebecausethereissomuchuncertaintyinthe modellingthatmakesthesimulationrelatestoreality.Itisalso thecasethatoptimizationisnotsomuchaboutfindingthe“best” solution,butasmuchaboutexploringthedesignspacefor alterna-tivesolutions.Evolutionaryalgorithmsarerobustinexploringthe searchspaceforawiderangeofbuildingoptimizationproblems. Unlikemanyotherconventionalorheuristicalgorithms, Evolution-aryAlgorithmsarealsoeasilyadaptedtoenablethemtosolvea particularoptimizationproblemmoreeffectively.Moreover,the riseofsimulationhasbeendrivenbymanythings,including gov-ernmentpolicythatpushesthedesignofNZEBs.Atpresent,any increaseintheuseofoptimizationwillbedrivenbytheextentto whichaidsdesigndecisionmaking.Inthisrespect,oneofthemost powerfulforms optimizationismulti-object optimization,since thisprovidesasetofsolutionsthatlieonthetrade-offbetween twoorconflictingdesignobjectives.Thetrade-offcanbeusedto exploretheimpactofsayoflesscapitalinvestmentontheincrease incarbonemissions(thiskindofinformationbeingusefulin deci-sionmaking).However,decisionsupport,time,knowledge,lackof tools,anduncertaintywerethethemesthatranthroughthe experi-encesoftheinterviewedexperts.Thefactorsthatinhibittheuptake ofBPOarenotonlyrelatedtotheoptimizationtechniquesorthe toolsthemselves,butalsotothesimulationmodelsinputs,causing significantrestrainintheAECindustrytake-up.Interviewees’ opin-ionsaboutBPO,andtheirsubsequentexperiences,werefoundto bemostlyinfluencedbytheirresearchworkandcommunity.From theevidenceavailable,theoptimizationprocessdidnot,ingeneral, seemtobesystematicanddesigncentred,apartfromasmallgroup ofexpertswhousedBPOinrealdesignpractice.

4.2. Strengthsandlimitationsofthestudy

Themethodologyusedinthisstudyliteraturereviewand struc-turedinterviewswasappropriatetogeneratehypothesesfroma largepopulationsample.Verbatimtranscriptionswereundertaken andselectedquotationswerenotedited(Attia,2012).Therewas independentanalysisofthedataandconcordanceinthe identi-ﬁcationofthemes.Thechoiceofsetting,IBPSAandIEA,allowed expertstoberecruitedfrompracticeswhorepresentarangeof NZEBandsimulationgroups.Furthermore,theexpertsformeda representativesampleintermsof theoutcomesrelatedtoBPO. Theexpertsweremadeawareatthebeginningoftheinterviews thattheinterviewerwasaresearcher,architecturalengineerand IEASHCTask40/ECBCSAnnex52members.Whilethisknowledge mayhavebeenhelpfulinallowingexpertstofeelcomfortablein

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anAECsetting,therebyfacilitatingdiscussionsaboutbuilding per-formancerelatedmatters,thisknowledgemayhavehadanimpact onthedata.Speciﬁcally,theexpertsmayhavefeltobligedtoalign theirviewswithwhattheyperceivedtobetheestablishedIBPSA standpoint,forinstanceofferingamorepositiveopiniononBPO thantheywouldhavedoneotherwise.

Thenumberoftheexpertgroupmeansthatstatistical represen-tationcannotbeclaimed.Furthermore,itwasnotpossibletoensure thattheexpertrepresentedadesiredbroadrangeofoptimization groups.The samplingstrategywastherefore prospectiverather thanpurposive,anditwould havebeenpreferabletointerview moreexpertswhodeclinedthetestandexpertswhodonotspeak English,asalloftheinterviewedwereEnglishspeakers.Finally,it wouldhavebeenpreferabletointerviewmoreexpertswhowork inpractice.

4.3. Implicationsforpracticeandfutureresearch

Theﬁndingthat BPO is surroundedbyissues of uncertainty imposesnewobligationsonresearchersandsoftwaredevelopers. Thisinvolvesembracingmoredesign-centredoptimizationworkin additiontosettingsystematicframeworksofperforming optimiza-tionfordesigndecisionsupport,uncertaintyandcommunication, andoptimization-basedbuildingsolutions.Moreover,reliableand accurateinformationonbuildingperformanceiscrucialforexperts tocreaterobustinformeddesignchoices.Optimizationperformed fordesignersneedstoexplaintheimpactsonthedesignquality bothbeforeandaftertheuseofoptimization,andtheassociated uncertaintiesneedtobediscussed.

Furthermore,recognitionis neededthatoptimizationis nec-essaryforcomplexNZEBs.Designersdonotrelyonoptimization sufficientlyduetothelackofpublicdomaindesignpackages inte-grated with open domain, object oriented analysis tools. They are also influenced, often strongly, by the design complexity, limitedtimeandinvestmentpressure.Policymakersmust there-forerespondaccordinglyandrecognizethatBPOdoesnotstartand finishintheresearchlabs.BPOcouldberequiredasa standard activityduringNZEBsdesignandoperationandmadeavailablein arangeofpublicNZEBdesignpractice.Thegreatestpossibilities, however,areaffordedbytheresearchers.Notwithstandingthereal issueofcomputationtimeandtheseamlessintegrationof simula-tionandoptimizationmodelwithdesignmodels.Ultimatelyinthe future,alldesignersparticipatinginthedesign(architects, engi-neers,etc.)willbeinvolvedinusingBPOtechniques.Optimization isaboutpresentingdesignalternativestothedesignerregardless ofwhomtheyare.Soitmightbethatthearchitecthasadifferent setoftoolsanditisadifferentoptimizationmethodologybutthey willnotbeexcludedfromusingoptimization.

Atpresent,theintegrationofBPO intothedesign processis aresearchissue.WhilethissampleofexpertsconﬁrmsthatBPO willaddvaluetothedesignwedonothavetheproof.Ifwehave solidproofdesignerswillbeverylikelyuseoptimizationtechniques becauseitenhancesthebuildingstheyaredesigning,sotheycan getbetterbuildings.Moreresearchisneededontheexperienceof designerswithBPO.Researchhastoshowdesignersthattheuse ofBPOproduceresultsbetterthantheirdesign.Thiswouldalso allowthedevelopmentofbetterBPOtoolsthatarebothaccurate andsupportthedecisionmaking.

Acknowledgements

TheauthorsexpresstheirthankstoScottBucking,RemiCharon, RuchiChoudhary,BrianCoffey,ChadCorbin,NatasaDjuric,Elaine Hale,MohamedHamdy,AlaHassan,JanHensen,GregorHenze, Les-leyHermann,Pieter-JanHoes,ChristinaHopfe,DirkJacob,Michael

Kummert,NicholasLong,LaurentMagnier,PeterT.May-Ostendorp, MonjurMourshed,TatsuoNagai,MattiPalonen,ChristianStruck, MikaVuolle,WeiminWang,MichaelWetter,JonathanWrightand YiZhangandappreciatetheirvaluablecommentsandfeedback. TheauthorsthankJosefAyoub,NaturalResourcesCanada,Andreas Athienitis, Concordia University and IEA SHC Task40/ECBCS Annex 52, Subtask Bfor comments on earlier versions of this article.

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