Advances and challenges in building engineering and data mining applications for energy-efficient communities

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ContentslistsavailableatScienceDirect

Sustainable

Cities

and

Society

j o ur n a l ho me p a g e :w w w . e l s e v i e r . c o m / l o c a t e / s c s

Engineering

advance

Advances

and

challenges

in

building

engineering

and

data

mining

applications

for

energy-efﬁcient

communities

夽

Zhun

(Jerry)

Yu

a

_,

_Fariborz

_Haghighat

b,∗

_,

_Benjamin

_C.M.

_Fung

c a_College_of_Civil_Engineering,_Hunan_University,_Changsha_410082,_Hunan,_PR_China

b_Department_of_Building,_Civil_and_{Environmental}_Engineering,_Concordia_University,_Montreal,_QC,_Canada_H3G_1M8 c_School_of_Information_Studies,_McGill_University,_Montreal,_QC,_Canada_H3A_1X1

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Availableonline19December2015 Keywords:

DataMining BuildingEnergyUse OccupantBehavior BigData Review

a

b

s

t

r

a

c

t

Therapidlygrowingandgiganticbodyofstoreddatainthebuildingfield,coupledwiththeneedfordata analysis,hasgeneratedanurgentneedforpowerfultoolsthatcanextracthiddenbutusefulknowledge ofbuildingperformanceimprovementfromlargedatasets.Asanemergingsubfieldofcomputerscience, dataminingtechnologiessuitthisneedwellandhavebeenproposedforrelevantknowledgediscovery inthepastseveralyears.Aimedtohighlightrecentadvances,thispaperprovidesanoverviewofthe studiesundertakingthetwomaindataminingtasks(i.e.predictivetasksanddescriptivetasks)inthe buildingfield.Basedontheoverview,majorchallengesandfutureresearchtrendsarealsodiscussed.

Contents

1. Introduction...33

2. DMapplicationsinthebuildingﬁeld...34

2.1. Predictivetasks...34

2.1.1. Buildingenergydemandprediction...34

2.1.2. Buildingoccupancyandoccupantbehavior...34

2.1.3. Faultdetectiondiagnostics(FDD)forbuildingsystems...34

2.2. Descriptivetasks...35

2.2.1. Frameworkdevelopment...35

2.2.2. Occupantbehavior...35

2.2.3. Buildingmodelingandoptimalcontrol...35

2.2.4. Discoveringandunderstandingenergyusepatterns...36

3. Futuredirectionsandchallenges—Concludingremarks...36

Acknowledgements ... 37

References...37

1. Introduction

Currently theimportanceof improvingbuildingenergy per-formanceforsavingenergyandenhancingbuildingsustainability hasbeenwidelyrecognized.Oneeffectivewayofachievingthis objectiveistouncoverandextractusefulknowledgefrom build-ing operational data (e.g. temperature, ﬂow rate, power and

夽 ThisisanEngineeringAdvancepaper. ∗ Correspondingauthor.

E-mailaddress:[email protected](F.Haghighat).

equipmentstates) that contains abundantvaluable information onactualbuildingperformance. Thewidespreaduseofbuilding automationsystems(BASs)enablesatremendousamountof build-ingoperationaldatatobestoredinbuildingdatabasesthatalso continue to expand. Thisrapidlygrowing and giganticbody of storeddata,coupledwiththeneedfor dataanalysis,has gener-atedanurgentneedforpowerfultoolsthatcanextracthiddenbut usefulknowledgefromlargebuildingdatabases.

Asanemergingandpromisingtechnology,datamining(DM) isapowerfulandversatiletooltoautomaticallyextractthe valu-able knowledge embeddedin huge amounts of data.It can be deﬁnedinmanydifferentways.AsdeﬁnedbyCabena,Hadjinian,

http://dx.doi.org/10.1016/j.scs.2015.12.001

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Stadler,Verhees,and Zanasi(1998),DMis “aninterdisciplinary fieldbringingtogethertechniquesfrommachinelearning,pattern recognition,statistics,databases,andvisualizationtoaddressthe issueofinformationextractionfromlargedatabases.”Inthepast severaldecades,researchershavebeenvigorouslyandsuccessfully applyingDMinmanyscientific,medical,andapplicationdomains suchasbanking,bioinformaticsandnewmaterialsidentification. Recentlyithasalsobeenintroducedintothebuildingfieldthatisa well-fitapplicationareaforDM,sinceitgeneratesandcollectsvast amountsofdataonsystemoperation,occupantbehavior,power consumption,climaticconditionsandetc.

Ingeneral,DMincludessixcategoriesofwidelyacceptedand implementedtechniques(Usama,Gregory,&Smyth,1996):

• Dataclassiﬁcation(e.g.thedecisiontreemethod,supportvector machine(SVM)andartiﬁcialneuralnetwork(ANN)),

• Clusteringanalysis,

• AssociationRuleMining(ARM), • Regression,

• Summarization,and • AnomalyDetection.

Readersmay refer to(Jia,Kamber, &Pei, 2012) for detailed informationofthesetechniques.Thesetechniquescanbebroadly categorizedintopredictivetasksanddescriptivetasks(Jiaetal., 2012).Thispaperreportsanoverviewoftherecentstudies under-takingthetwotasksinthebuildingﬁeld.

2. DMapplicationsinthebuildingﬁeld

2.1. Predictivetasks

2.1.1. Buildingenergydemandprediction

Thepredictionofbuildingenergydemandplaysanimportant rolein improvingbuildingperformance. Anaccurateprediction needstotakevarioussignificantinfluencing factors ofbuilding energydemand intoconsideration, suchasweather conditions, HVACequipment,buildingenvelopesandoccupantbehavior.The complexityanduncertaintyofthesefactorsfurtheradds difficul-tiestoimprovepredictionaccuracy.DMtechniquesmaymakea breakthroughindealingwiththiscomplexityanduncertainty.

Zhaoand Magoulès(2012)indicatedthatDMtechniquesare veryapplicabletobuildingenergydemandpredictionsincethey candeal withnon-linear problems. ANNand SVM arethe two most widely used DM techniques for this application (Ahmad etal.,2014).Kumar, Aggarwal,andSharma(2013)applied vari-ousANNmethods,includingbackpropagation,recurrentANN,auto associativeANNandgeneralregression ANN.TheadoptedANN architecturesignificantlyinfluencesthecoefficientofvariationthat rangesfrom2%to40%.TheyconcludedthatANNismoresuitable forthepredictionofalargesetofparametersthananystatistical techniques.Li,Ren,andMeng(2010)reportedthatinmanycases SVMshowshigherpredictionaccuracythanANN.However, train-ingSVMcanbeaveryslowprocessduetolargevolumeoftraining data.TheusageofparallelSVM(Zhao&Magoulès,2011)mightbe afeasiblealternative.

BothANNandSVMmodelsoperatelikea“blackbox”, mean-ingthatthemodelcanprovideapredictionbutcannotprovidea justiﬁcationforsupportingtheprediction.Inordertoovercome thislimitation,Yu,Haghighat,Fung,andYoshino(2010)developed abuildingenergy demandpredictivemodel basedonthe deci-siontreemethod.Itscompetitiveadvantageliesintheabilityto generateaccuratepredictivemodels(92%accuracyintheirstudy) withinterpretableﬂowchart-liketreestructuresthatenableusers toquicklyextractusefulinformation.However,thedecisiontree

methodisbasicallydevelopedforpredictingcategoricalvariables otherthanforpredictingnumericalvariables.

ConsideringthatdifferentDMtechniqueshavetheirown spe-cialties,strengthsandweaknesses,itisnoteasytoﬁndthebest candidateforthebuildingenergydemandprediction.Therecent researchtrendis towardstheintegrationof differentDM tech-niquesformoreaccurateprediction.For example,ChouandBui

(2014) developed an ensemble model by combining ANN and

SVMtopredictcoolingand heatingdemand.Inordertopredict thenext-dayenergyconsumptionandpeakpowerdemand,Fan, Xiao,andWang(2014)developedensemblemodelsbycombining eightbaseDMmodelsandassigningeachDMmodalanoptimized weightbasedongeneticalgorithm(GA).Theresultsshowthatthe ensemblemodelsachievehigherpredictionaccuracythanthoseof individualbasemodels.

2.1.2. Buildingoccupancyandoccupantbehavior

Buildingoccupancyandoccupantbehavior arerecognizedas crucialfactorsinﬂuencingthediscrepancybetweenpracticaland simulatedbuildingenergyconsumption.However,itisdifﬁcultto investigatethemanalyticallyandthentodevelopreliable predic-tionmodelsduetotheircomplicatedcharacteristicsandstochastic nature(Yu,Haghighat,Fung,Morofsky,&Yoshino,2011a).Tomeet thischallenge,differentstochasticmodels(e.g.usingprobabilistic methods(Sun,Yan,Hong,&Guo,2014;Stoppel&Leite,2014)and theMarkovChainmethod(Muratori,Roberts,Sioshansi,Marano,& Rizzoni,2013))havebeenproposed.However,theproposed mod-elsareseverelyrestrainedbythelimitation:Themodelingprocess tendstobecomplex and advanced mathematicalknowledge is required.

In order to remove the above limitation, researchers have attempted to establish DM-based models. For example, Basu, Hawarah,Arghira,Joumaa,&Ploix, 2013)developeda decision-treebasedmodelforpredictingoccupantbehaviorattheappliance levelinresidentialbuildings.D’OcaandHong(2015)proposeda DMmethodologytomodelofficeoccupancypatternsand work-inguserprofilesbasedonbigdatastreams.Theyfoundthatthe decisiontreemethodissuitableforpredictingtheoccupancy pres-ence,supportedbyZhao,Lasternas,Lam,Yun,andLoftness(2014), whobuilttheoccupantbehaviorpredictionmodelsbasedon appli-ancepowerconsumptiondatainamedium-sizeofficebuilding.The resultsofbothstudiesindicatethatthemodelingaccuracyisvery satisfactory.However,thedevelopeddecision-treebasedmodels arestaticmodelsthatarehardtosimulatethedynamicnatureof buildingoccupancyandoccupantbehavior.

Themainfocusoffutureresearchshouldbeplacedontesting andcomparingotherdynamicDM-basedmodelsandintegrating themintobuildingenergy modelingprogramslikeTRNSYSand EnergyPlus.Inaddition,moreresearchneedstobeconductedso thatarchitectsanddesignerswillbeneﬁtfrombridgingthegap betweenactualandpredictedbuildingenergyperformance.

2.1.3. Faultdetectiondiagnostics(FDD)forbuildingsystems Automatingthe process of detectingequipmentand system malfunctionsandmakingaproperdiagnosiscanhelptoensure stableoroptimalbuildingoperation.Intermsoftheapproachto formulatingthediagnostics,FDDmethodscanbecategorizedas model-based methods, which are based onprior knowledge of underlyingsystemphysics,anddata-drivenmethods,whichare basedonhistoricaldata(Katipamula&Brambley,2005).

Therequirementof priorknowledge,togetherwithcomplex modeling processes and heavy computationalburden, imposes severeconstraintsontheapplicationofthemodel-based meth-ods.Comparatively,thedatadrivenmethodsaremucheasiertouse sincethemodelsarenormallyautomaticallygenerated.VariousDM techniqueshavebeenemployedasdatadrivenmethodsforFDD.

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Forexample,Magoulès,Zhao,&Elizondo,2013)developedanANN modelbasedontherecursivedeterministicperceptron(RDP)ANN toimplementFDDatthewholebuildinglevel.Moreover,they pro-posedanewfaultdiagnosticprocedureforidentifyingandranking abnormalequipmentintheorderoffaultrisk.OtherDMtechniques havealsobeenemployedforFDDsuchasclusteranalysis(Khan, Capozzoli,Corgnati,&Cerquitelli,2013),fuzzylogic(Lauroetal., 2014)andSVM(Mulumba,Afshari,Yan,Shen,&Norford,2015).

In order to takeadvantage of different DM techniques and achieve more robust models, hybrid approaches have been proposedforFDDsuchasthecombinationoftheSVMand autore-gressivemodelwithexogenousinputs(ARX)(Yan,Shen,Mulumba, &Afshari,2014)andthecombinationofANNandsubtractive clus-tering analysis(Du,Fan, Jin, &Chi,2014).Experimental results suggestthesehybridapproacheshavehigherpredictionaccuracy andlowerfalsealarmratesthantheapplicationofsinglemodels. However,theaboveDM-basedmodels,bothindividualandhybrid, focusedonhistoricaldataandcannotbeusedforreal-timeFDD.

Recently,researchers haveshown interestin developing on-lineFDDtoolsthatcanprovidereal-timeornear-real-timeupdates onmodelparametersandpredictions(Bonvini,Sohn,Granderson, Wetter, & Piette, 2014). DM techniques, particularly the SVM, have been utilized in these studies and shown great potential forreal-timeFDDsincetheypreventperformingnumerous time-consumingsimulations(Yanetal.,2014).Futureresearchshould focusonverifyingpredictionofmodelsbasedonarealworlddata set.

2.2. Descriptivetasks

2.2.1. Frameworkdevelopment

ThediversityofDMtechniquesandtheirfunctionalities neces-sitatesestablishingandprovidingaframeworkforuserswhomay nothavemuchknowledgeandexperiencewiththeapplicationof DM.Basically,suchframeworkneedstospecifywhichDM tech-nique/algorithmcanbeusedforbuildingapplicationandwhich kindofpatterncanbeminedcorrespondingly.Theultimate objec-tiveistohelpuserstodevelopasetofdataanalysismethodologies sothattheycansolvetheproblemsbasedontheframework. More-over,casestudiesalsoneedtobeconductedinordertodemonstrate itseffectiveness.Theframework,togetherwiththecasestudies, enablesuserstoanalyzebuilding-relateddatamoreefﬁciently.

BothgeneralandspecificDMframeworkshavebeenproposedin thepastseveralyears(Yu,Fung,&Haghighat,2013).General frame-worksprovideaDMtechniquepoolanddifferentpatternswillbe foundgivendifferentDMtechniquesareselectedandcombined. Specificframeworksaretargetedatexploringa specificpattern suchas occupancypatternsand designated DMtechniquesare used.

Regarding general frameworks, Yu et al. (2013) estab-lished a four-component framework consisting of DM tech-niques/algorithms.Theyalsoproposedastep-by-stepdataanalysis processthatstartsfromproblemdeﬁnitiontoknowledge discov-ery.Fan,Xiao,andYan(2015)developedaframeworkthatmainly includesfourphases:dataexploration,datapartitioning, knowl-edgediscoveryandpostmining.Regardingspeciﬁcframeworks,

D’OcaandHong(2015)developedaframeworktodiscover occu-pancy patterns and working user proﬁles in ofﬁce spaces. The decision-treemethodandclusteranalysiswerecombinedto pro-videinsightsintopatternsofoccupancyschedules.However,these frameworksinvolvelimitedDMtechniquesandsofarrelatively veryfewproblemshasbeentackledwiththeinvolvedDM tech-niques.

DMframeworkscandeliverasetofdataanalysismethodologies topracticaloperation,andthusefﬁcientlygeneralizingtheusage ofDMtechniquestoawiderpopulation.Theyprovideanoverview

andastandardizedprocessoftheusageofDMtechniques. There-fore,moreframeworks,particularlyspeciﬁcframeworks,andcase studieswithhighreplicationpotentialshouldbedevelopedand appliedfordifferentapplications.

2.2.2. Occupantbehavior

DMtechniqueshavealsobeenemployedtoconductdescriptive tasksforoccupantbehaviorinseveralstudies.Thesestudiesfocus oneitheronetypeofspeciﬁcbehaviorinofﬁcebuildingsorfull patternsofbehaviorinresidentialbuildings.Theirmainobjectives aretodiscoverbehaviorpatternsandtoidentifyandimprovethe effectofoccupantbehavioronbuildingenergyconsumption.The obtainedknowledgecanbeintegratedintobuildingenergy simu-lationprogramsespeciallywhensettingupbehaviorschedules.

D’Oca and Hong (2014) combined clustering analysis with

ARM in order to discover occupants’ window opening/closing behavior patterns in a natural ventilatedofﬁce building. Based onclusteranalysis,Yu,Fung,Haghighat,Yoshino,andMorofsky

(2011b)developedanewmethodologyforexaminingthe

influ-encesofoccupantbehavioronbuildingenergyconsumptionwhile removingtheeffect ofother factorssuchasclimateand build-ing envelopes.Based on clusteranalysis, classification analysis, andARM, Yuetal.(2011a) developedanothermethodologyfor identifyingenergy-inefficientbehaviorinresidentialbuildingsand providingoccupantswithfeasiblerecommendationstoimprove them. It is worth mentioning that the effective usage of DM techniques,particularlyclusteranalysis,for describingoccupant behaviorisheavilydependentonthesizeofestablisheddatabases, whichnormallyarerestrictedbyvariousfactorssuchasbudgets andeffortinpractice.

Duetoitscomplexityandrandomness,theinteractionsbetween occupant behaviorand otherinﬂuencing factorsare stillpoorly understood.FurtherstudiesontheDM-basedmethodologiesare activelyencouraged,andthecombinationofclusteranalysiswith ARMdeservesextraattention.

2.2.3. Buildingmodelingandoptimalcontrol

Buildingoperationaldatacontainsvaluableinformationthatcan beextractedformodelingandidentifyingthehiddenrelationship betweendifferentvariables,therebyimprovingbuildingoperation. Also,couplingtheinformationintobuildingcontrolsystemscan assist in optimizingbuildingperformance. Anumber of studies havealreadybeenconductedonsuchinformationextraction.

Withthegoal ofimprovingbuildingperformance, DM tech-niques have been employed to model different performance variablessuchasfurnaces’cycleidletime(Li,Miao,&Shi,2014), thermodynamicproperties of refrigerants (Küçüksille,Selbas¸, & S¸encan,2011),applianceusage(Basu,Debusschere,&Bacha,2012) andcoefficientofperformanceforrefrigerationequipment(Chou, Hsu,&Lin,2014).Forexample,Ahmed,Korres,Ploennigs,Elhadi, and Menzel(2011)usedNaïveBayes,decision treeandSVM to simplifytheproceduretoestablishmodelsforpredictinginternal naturallightingandthermalcomfort.Theresultsshowthehigh predictionaccuracyandreliabilityofthesetechniques.Basedon theBASdatabaseofthetallest buildinginHongKong, Xiaoand Fan(2014)firstappliedclusteringanalysistoidentifythetypical buildingenergyconsumptionpatterns,andthenappliedARMto eachclusterfordiscoveringthehiddenassociationsamongpower consumptionsofmajorequipmentsuchaschillersandpumps.Asa result,deficitflowinthechilledwatersystemwasfoundand abnor-maloperationoftheprimaryandsecondarypumpswasdetected. DMtechniqueshavealsobeenemployedforHVACsystem opti-malcontrolwiththeaimofminimizingtheenergyconsumption while maintaining indoorcomfort levels. Generally these tech-niquesareintegratedintocontrollersinordertotakeadvantageof theirstrengths.Forexample,basedonthefuzzylogicmethod,ANN

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andGeneticAlgorithm,fuzzylogiccontrollers(Hussain,Gabbar, Bondarenko, Musharavati, & Pokharel, 2014), ANN controllers (Moon, Lee,&Kim, 2014)andGA-based controllers(Congradac &Kulic, 2009)were developed. These controllers have already been widely applied in the buildingﬁeld and a large number of studies can be found in literature that is not listed in this paper.

Existingstudieshavealreadydemonstratedthecapabilitiesof DMtechniquesasameanstoeffectivelyhelpimprovingand opti-mizingbuildingperformance.Potential futureresearchincludes developingmoreaccurateandrobustmodels,particularlya com-binationofminingtechniques,that canbereadilyusable. Also, morepractical applicationsofDM-based controllersneedtobe conductedinthebuildingﬁeldinordertoevaluatetheirusability.

2.2.4. Discoveringandunderstandingenergyusepatterns

Buildingenergyconsumptionpatterns,bothfrequentand infre-quent,usuallycanbeusedtohelpidentifyoperationabnormalities andfaults.Effectivepatternrecognitionandcorresponding knowl-edgeextractionbyminingbuildinghistoricaldataorsimulation datacanleadtoenergysavingsandperformance improvement. Relevantstudiesatboththewholebuildinglevelandsub-system levelhavebeenconductedinrecentyears.

Atthewholebuildinglevel,Miller,Nagy,andSchlueter(2015)

appliedclusteringanalysistoaggregatefrequentenergyuse pat-ternssoastogenerateproﬁlesthatmaybeneﬁtfuturesimulation modelcalibration.Lange, Rodriguez,Puech,and Vasques(2013)

developeda3DvisualizationtoolthatcanbeintegratedintoBASs tovisualizedatainanunderstandableway.Theworkispartofthe RIDERprojectthatcomposedofasetofDMtechniquesaimedat gaininginsightintobuildingenergyusepatterns.BasedonARM,

Yu,Haghighat,Fung,andZhou(2012)developedamethodology for examining all associations and correlations between build-ingoperationaldata,therebypinpointingenergywasteaswellas equipmentfaults.Byminingsimulationdata,Kim,Stumpf,andKim (2011)developedanoveralldataanalysisprocesstoﬁndimportant patternsinordertoimprovetheenergyefﬁciencyofbuildingdesign duringtheearlydesignphase.

Atthesub-systemlevel,anincreasingnumberofstudies(Lai, Lai,Huang,&Chao,2013;Beckel,Sadamori,Staake,&Santini,2014; Rollins&Banerjee,2014)focusedontheinvestigationson appli-anceusagepatterns.Chicco(2012)provided anoverviewofthe previousliteratureonclusteringanalysisforgroupingand min-ingelectricalloadpatterns,particularlyresidentialdemandproﬁles (Rhodes,Cole,Upshaw,Edgar,&Webber,2014).Fewstudiesfurther focusedonmorespeciﬁcapplianceusagepatternssuchaslighting (MottaCabrera&Zareipour,2013).

Patternsextractedfrombuildinghistoricdataandsimulation dataprovideadeepinsightintothewaybuildingoccupants con-sumeenergy.Utilities,policy-makers,buildingowners,designers andoperatorsmaybeneﬁtgreatlyfromtheseextractedknowledge. Mostexistingstudiesarestillfocusedongeneralpatternanalysis overalongtimelength,limitedbylowresolutiondataavailable.In ordertodiscoverin-depthknowledgeofbuildingperformance,the focusoffuturestudiesneedstobeplacedonmoredetailedpatterns basedonhighresolutiondatasuchas1-mindata.

3. Futuredirectionsandchallenges—Concludingremarks

Thispaperreviewstheadvancesintheﬁeldofbuilding engi-neeringand DMapplicationsover thelast severalyears. It has beenshownthatDMtechniquesareaneffectivetoolforextracting hiddenbutusefulknowledgefrombuilding-relateddata,thereby improvingbuildingperformance.However,someimportant prob-lemslimitingtheapplicationsstillneedtobeaddressedandmore

effort is needed. The following described theencountered and anticipatedchallenges,potentialfuturedevelopmentsand recom-mendationsfortheapplicationofDMtechniquestothebuilding ﬁeld:

(1)Low-qualitydataleadstolow-qualitydataminingresults.A keychallengeishowtoimprovedataqualityintermsof relia-bility,completeness,consistencyandresolution.Therearetwo major reasonsfor thelack of high-qualitydata: thelack of widely-acceptedbenchmarksforidentifyingtheparametersto bemonitoredandthelackofadequatesensorsforcollecting reliableandhighresolutiondatathattendtobecostly. More-over,forresidentialbuildings,theenergyconsumptiondataare collectedfromthemonthlyorannualbillsfromutility compa-nies.Thesehighlysummarizeddataareinsufﬁcienttoconduct detaileddataanalysis.Thefactthatend-usedataonhousehold applianceusageisseldommonitoredaddsgreatdifﬁcultiesto in-depthdataanalysis.

(2)Wideanddiverseapplicationshavebeenfoundandvarious DMtechniqueshavebeenemployed.However,consideringthat DMisarelativelyyoungandfast-growingdisciplineandnew techniquesemerges,clearlythepotentialofDMtoextract use-fulknowledgefrombuilding-relateddatahasnot beenfully exploited.Inthisview,buildingresearchersandengineersneed tokeepaneyeonthecurrenttrendtowardsthedevelopmentof DMtechniques,suchasscalableandconstraint-basedmining methods,aswellastheirapplications.

(3)Previous studies indicate that theapplications have several importantlimitations.Forexample,althoughadeeper under-standing of the data can be gained, the process of model constructionandknowledgeextractionreliesheavilyonusers’ priordomainknowledge(e.g.settingathresholdforARMand determiningtheoptimalnumberofclusters).Also,itisnot intu-itiveonhowtoutilizethediscoveredknowledge.Moreeffortis neededfortheexplorationofimprovingtheefficiencyandease oftheminingprocessandresultanalysis.Onepossibledirection totakeistheintroductionof“actionmining”or“profit min-ing”thathasshownsomesuccessinthebusinessworld.Their DMresultsarenotjustabunchofrules,butsomesuggested actionstomaximizetheprofitorrevenue.Similarconceptsmay beapplicableinthebuildingfield.

(4)Theincreasingvolume,velocityandvarietyofbuilding-related data usually result in databases with sizesand complexity beyondtheabilityoftraditional statisticalanalysismethods to process them efficiently. Such databases can be consid-eredas“BigData”thatisacurrenttopicunderdiscussionin Information, Communicationand Technology(ICT) in build-ings’experts.Basedonthedefinitionandexistingapplication of DM to building-related data,DM is expected to play an importantroleinestablishingeffective“BigData”management approaches.Fromthisperspective,itishighlyrecommended thatfutureresearchpaymoreattentiontothefollowingpoints: • PassingtheinformationontheusefulnessofDMapplications inthebuildingfieldtopolicymakersandbuildingownersso thattheywill,inturn,besupportiveformoreapplications; • formulating acceptable benchmarks for identifying the

parameterstobemonitored;

• lowering the cost of monitoring and accessing building-relateddata;

• developingmethodologiesbasedonnewDMtechniquesfor diverseapplications;

• improvingthepresentationandvisualizationofDMresults; and

• investigating multiple building blocks and communities besidesindividualbuildings.

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Acknowledgements

ThisworkwasﬁnanciallysupportedbytheNationalNatural Science Foundation of China (No. 51408205), the Fundamental ResearchFundsfortheCentralUniversities,HunanProvincial Sci-ence and Technology Department Major Project of China (No. 2011FJ1007)andwasalsofundedbyELS.

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