Behavioral patterns and profiles of electricity consumption in dutch dwellings

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Delft University of Technology

Behavioral patterns and profiles of electricity consumption in dutch dwellings

Bedir, Merve; Kara, Emre C.

DOI

10.1016/j.enbuild.2017.06.015

Publication date

2017

Document Version

Final published version

Published in

Energy and Buildings

Citation (APA)

Bedir, M., & Kara, E. C. (2017). Behavioral patterns and profiles of electricity consumption in dutch

dwellings. Energy and Buildings, 150, 339-352. https://doi.org/10.1016/j.enbuild.2017.06.015

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ContentslistsavailableatScienceDirect

Energy

and

Buildings

jo u r n al h om ep a 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

Behavioral

patterns

and

proﬁles

of

electricity

consumption

in

dutch

dwellings

Merve

Bedir

a,∗

_,

_Emre

_C.

_Kara

b

a_Delft_University_of_Technology,_Faculty_of_{Architecture,}_Department_of_{Architectural}_Engineering₊_Technology,_Design_Informatics,_Julianalaan_134,₂₆₂₈

BLDelft,TheNetherlands

b_Department_of_Civil_and_{Environmental}_Engineering,_Carnegie_Mellon_University,₅₀₀₀_Forbes_Ave,_Pittsburgh,_PA_15213,_USA

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received20February2017

Receivedinrevisedform12May2017 Accepted10June2017

Availableonline12June2017 Keywords:

Energyefﬁciency Occupantbehavior

Behavioralpatternsandproﬁles Electricityconsumption Dutchhouseholds

a

b

s

t

r

a

c

t

InEUmemberstates,theconsumptionofelectricityperdwellinghasremainedmoreorlessconstant, althoughtheconsumptionoflargeapplianceshasdecreasedconsiderablyinthelast2decades.This stabilizationiscausedbytheincreasedownership,usageandconsumptionlevelsofInformation, Com-municationandEntertainment(ICE)appliances.Thispaperaimstoanalyzeelectricalapplianceuseinthe Dutchhousingstock,andidentifybehavioralpatternsandprofilesofelectricityconsumption.The anal-ysisisconductedbyapplyingdescriptive,correlation,andexploratoryfactoranalysesondatacollected from323dwellingsintwoneighborhoodsintheNetherlands.Ourresultsshowthatbehavioralpatterns couldbefoundbasedonactualoccupantbehavioroflightingandapplianceuse,especiallydepending onhouseholdactivitieslikecooking,(personal)cleaning,etc.Behavioralprofilescouldbedetermined basedonhouseholdanddwellingcharacteristics,i.e.householdsize,income,education,dwellingtype, age,hoursofworkingoutside.The4profilessetupinthisresearchareexplainedas‘family,’‘techie,’ ‘comforty,’and‘conscious.’Theseprofilesshowedstatisticallysignificantlydifferencesintermsoftheir electricityconsumptionlevels.

1. Introduction

Residential buildings consume 23% of the electricity in the Netherlands[1].ODYSSEE-MUREprojectreportsthat,inEuropean Union(EU)countries,althoughtheconsumptionoflargeappliances hasdecreasedconsiderablybetween2000and2012(Fig.1(left)), increasingownershipanduseofappliancesandlargerhomespush theelectricityconsumptionupbyabout0.4%peryear,per house-hold[2].Household electricityconsumption in theNetherlands hasfolloweda similarpattern totheoneofEU (Fig.1 (center) and(right)).Whiletheefﬁcienciesofwashingmachine,dryer,dish washer,refrigerator,and freezerhaveimmenselyimprovedand theiruseremainedsimilar,thusreducingtheiroverallelectricity consumption;theownership,usagetimeandpowerofcomputer, printer,TV,DVD,and otherpersonal electronicdevices, electric oven, microwave oven, kettle, and similar have gone up, thus increasingtheiroverallelectricityconsumption[3].

These statistics point to the importance of the inﬂuence of occupants’ownershipanduseoflightingandappliances,and

sys-∗ Correspondingauthor.

E-mailaddresses:mb@landandcc.com(M.Bedir),ekara@stanford.edu (E.C.Kara).

temsontheelectricityconsumptionindwellings.Severalstudies haveclaimed thathouseholds canachievemore energysavings bychangingoccupantbehavior[4–8].Therefore,itisimportantto analyzetheshareofoccupantbehaviorinenergyconsumptionin detail.Moreresearchontheissueisneeded;however,thereare severalreasonstowhythisisdifﬁcult,someofwhicharethe retro-spectivemethodsofdatacollectionbytheenergycompanies,the assumedusagepatternsofsystemsandappliancesinmost calcu-lationtools,theuncertaintiesincollectingandanalyzingdata,the issuesofenergyperformancegap[9].

Inexistingresearch,behavioralfactorsrelatedtoheatingenergy consumptionhavebeenidentified,aswellasthehouseholdand dwellingcharacteristicsthatarerelatedtothesebehavioralfactors [10–12].Thestudiespointtothepotentialofenergyconsumption reduction,ifenergyefficiencypoliciesarearticulatedaccordingto differenthouseholdprofiles[10,13].Theabilitytomakeaccurate predictionsoftheelectricityusageofhouseholdsisanimportant issuenotonlyforpolicybutalsoforenergycompanies,andwill becomeevenmoreimportantwiththeemergenceofsmart elec-tricitygrids[9].

IntheNetherlands,variousstudieshavebeenconductedwith theaimofidentifyingbehavioralpatternsrelatedtohigherlevels ofheatingenergyconsumptionand/ortoenergy-savingattitudes, howeverthereisnosuchstudyforelectricityconsumption behav-http://dx.doi.org/10.1016/j.enbuild.2017.06.015

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Fig1.AverageelectricityconsumptionperdwellinginEU(left),ElectricityconsumptionoflargeelectricappliancesandTV(middle),Ownershipofappliancesinthe Netherlands(right).

ior.Ourworkcontributestotheliteraturebyproviding detailed informationaboutelectricityconsumptionbehavior,andby deter-miningthepatternsandproﬁlesofusers.Existingresearchsuggests that occupant behavior is more visible in newer than in older dwellings[12].Accordingly,oursamplemightbeappropriateto studyenergyconsumptionbehavior,becauseourdataiscollected ondwellingsbuiltafter1995.Inaddition,itseemsthatelectricity consumptionbehaviorrelatesfarless tothephysical character-isticsofahousecomparedtothatofheatingenergyconsumption, thereforeroutinesofelectricalapplianceusemightprovideuswith morearticulated insights intooccupant behavior. Thisresearch couldcontributetotheefforts,suchasWright’s[14],thatfocus onencouragingindividualsandhouseholdstowardsmoreenergy efﬁcientbehavior.

Inourpreviouspaper[9],wereportedonthevarianceinthetotal electricityconsumptionandresearchedthedeterminantsofitin dwellingsintheNetherlands.Wefoundthatusingtheparameters of duration of useof general, hobby,food, and cleaning appli-ances,householdsize,gasconsumption,yearsofresidence,number ofbedrooms,dwellingtype,numberofshowers,dryers,washing machineloads,andoutsideworkinghours,wecouldexplain58% ofthevarianceinelectricityconsumption.Inthispaper,weusethe samesampleanddataweusedinourformerwork.Ourfirstaim istofurtheranalyzethebehavioralaspectsofhousehold electric-ityconsumptionintheNetherlands.Forthis,westatisticallydefine behavioralpatternsandprofilesoflightingandelectricalappliance usageinrelationtoelectricityconsumption.Further,weidentify thehouseholdandbuildingcharacteristics,alongwithcluesabout lifestylesandattitudes,whichprovidetheevidencetobuild behav-ioralprofiles.

Ourdata iscollectedbya survey from323dwellingsinthe Netherlandson[1]applianceownership,[2]presence inrooms, [3]activitiesofcooking,showerandbath,cleaning,[4]household compositionanddwellingcharacteristics.Existingresearchfocuses eitheronbehavioralpatternsusingthefirstthreegroupsofdata, oronbehavioralprofilesusingthelastgroupofdata.Oursecond aimistolinkthepatternsandprofilesusingthebehavioral fac-torsasacommondenominator, foundbyfactoranalysis,which couldhelptobetterdefineoccupantbehaviorincalculationsand/or simulationprograms.

2. Literatureandresearchquestions

Behavioral patterns and proﬁles have been deﬁned with household characteristics [15–17], variables related to lifestyle [10,16–19], variables related to values, motivations, attitudes [11,20–23],and variables related mainlytoroutinesand habits [24–26].

Abreuetal.[27]adoptedaprofilerecognitionmethodtoidentify userprofilesofelectricityconsumption.Theelectricity consump-tiondatawascollectedwith15minintervalsfrom15housesovera periodrangingfrom3monthsto1year.Clusterswerethencreated usingprofilerecognitionoverthisquantitativedata.Households completedquestionnairestoself-reporttheirdailyroutines,and theusageprofilesthatwereobtainedwiththis‘qualitative’data werecomparedwiththe‘quantitative’clustersforvalidation.The studyshowedthatapproximately80%ofhouseholdelectricityuse canbeexplainedthroughrepeateddailyroutines.

Widenetal.[28]producedloadprofilesover5existing time-usedatasetscollectedinSwedenin1996,2006,and 2007.The numberofpeopleincludedinthesurveysvariedfrom13to431in 5to139households.Theactivitiesofpeoplewerereportednext tomeasurementsofelectricityand hotwaterconsumption.The dataresolutionvariedfrom5minto60min.Theactivityprofiles createdwithreporteddatawerecomparedtotheoneswith mea-sureddata.Theresultsshowedthathouseholdbehaviorprofiles regardingcooking,washing,lighting,TV,PCandaudiousecouldbe modeledusingtime-usedataofelectricityconsumption.However, hotwaterconsumptionwasnotsuccessfullymodeled.Itwasclear thatelectricityconsumptionwascloselyrelatedtooccupancyand thegroupingofappliancesaccordingtospecificactivities,andthis couldbeagoodwaytomodellingelectricityconsumption.

Colemanetal.[29]monitored14householdsintheUKbetween March2008 and August 2009. Thedwellings wereselected by snowballsampling,andtheyhadover220individualappliances. This researchfound that usage proﬁles varied widely between households in both sizeand make-up, and the average(mean) householdelectricityconsumptionfromICE(information, commu-nicationandentertainment)appliancesequatedtoaround23%of averagewholehouseelectricityconsumption(median18%).Ofthis, standbypowermodesaccountedfor11.5kWh,whichwasaround 30%ofICEapplianceconsumptionandaround7%ofaveragewhole houseelectricityconsumption.Colemanetal.foundthatdesktop computersandtelevisionsweretheappliancesthatconsumedthe mostelectricity,withmostoftheirconsumption occurring dur-ingtheactivepowermode.Audioappliances,printers,andother playandrecordequipmentweresigniﬁcantend-uses,largelydue tostandbyconsumption.Inoneofthehouseholds,computersthat werecontinuouslyactiveandconnectedtotheinternetwerealso foundtoberesponsibleforalargeportionofthesample’selectricity consumption.

O’Doherty etal. [30]analyzed thedeterminantsof domestic electricalapplianceownershipintheIrishhousingstock.Asurvey conductedin2001and2002on40,000housesrevealedthatnewer andmoreexpensivehouseshadmoreappliances,butalsomore EnergySavingAppliances(ESA).Yearsspentatthesameaddress

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Table1

Applianceuse:Ownershipandduration(minutesperday).

CONTINUOUSLYUSEDAPPLIANCES CLEANINGAPPLIANCES

App M Max Min SD App M Max Min SD

Internet(wirelessrouter) 1 3 0 .56 Dryer N 1 1 0 .47

D 19 130 0 28.18

Telephone 1 8 0 1.13 Iron N 1 3 0 .27

D 17 150 0 23.78

Fridge 1 2 0 .35 Vacuumcleaner N 1 3 0 .39

D 16 90 0 23.85

Freezer 1 2 0 .56 Wash.machine N 1 1 0 .18

D 50 90 0 D

FOODPREPARATIONAPPLIANCES ICEAPPLIANCES

App M Max Min SD App M Max Min SD

Coffeemachine N 1 3 0 .47 TV N 2 6 0 .89

D 32 840 0 76.10 D 238 900 0 161.87

Toaster N 1 2 0 .53 PC N 1 5 0 .82

D 3 85 0 7.11 D 153 2880 0 309.12

Electricgrill N 1 2 0 .46 Laptop N 1 6 0 1.08

D 14 255 0 23.77 D 190 3060 0 369.92

Microwaveoven N 1 2 0 .36 Stereo N 1 4 0 1.07

D 10 85 0 13.51 D 104 720 0 147.9

Waterheater N 1 2 0 .35 DVDplayer N 1 3 0 .68

D 13 85 0 14.54 D 21 360 0 40.92

Cookerhood N 1 2 0 .42

D 30 180 0 32.84

Dishwasher N 1 2 0 .43

D 42 240 0 45.33

(N:numberofappliance;D:durationofuse;M:mean;SD:StandardDeviation).

decreasedtheownershipofESA.Likewise,householdersunderthe

ageof40hadthemostappliancesbutalsothemostESA.Dwellings

locatedindenseurbanareashadmoreESA.Lastly,more

subur-ban,terracedhouseshadtheleastESA.O’Dohertyetal.’sgroups

weredeterminedbasedonhouseholdanddwellingcharacteristics

together,howevernorelationshipwasresearchedbetweenthese

groupsandelectricityuse.

Genjoetal.[31] usedclusteranalysistogroup505Japanese

householdsin1996.Thisresearchdidnotnecessarilytrytoidentify thespecificcharacteristicsofthegroupsaccordingtotheir electric-ityconsumption,butsomedistinctfindingsoftheirresearchwere thatthepossessionofelectricalapplianceswasareflectionof resi-dents’lifestyle,largerandmulti-functionapplianceswerepopular amongJapanesehouseholds,andeconomicaffluencehadastrong influenceingroupingthehouseholdsaccordingtoapplianceuse andelectricityconsumption.

IntheNetherlands,researchonbehavioralprofilesregarding energyconsumptionfocusonheatingenergy.Evenifthisresearch is only on electricity consumption, it is insightful to see and compareours’tothestudiesthatanalyzedheatingenergy con-sumption in terms of the household characteristics, behavioral factors, patterns and profiles. Raaij and Verhallen [10] identi-fied5profilesofenergybehavior among145householdsinthe Netherlands: Conservers (higher education, smaller household size),Spenders,Cool,Warm(oldestgroup)andAverage.Theyfound nodifferencesregardingincomeandemploymentparameters.The researchofGrootet al.[16] andPaauw etal. [17]developed 4 profilesofenergyconsumption:convenience/ease(comfort impor-tant,nointerestineconomicsavings,energy,ortheenvironment (EEE));conscious(comfortimportant,interestinsavingsforEEE), cost(awarenessofeconomyandhenceenergyandthe environ-ment); and climate/environment (concern for EEE). Raaij [10], Groot[16]and Paauw’s[17] workfoundstatisticallysignificant differencesin energyconsumption amongtheirgroups.Vringer etal.’swork[23]groupedhouseholdsintheNetherlands accord-ingtoincome,age,educationandhouseholdsize.GuerraSantin’s research[12]revealed5groups(spenders,comfort,affluent-cold,

Table2

Speciﬁcappliancesownedbyapercentageofhouseholds.

Appliancename Numberofhouseholds Percentageofhouseholds

inthesample

Electricalcooker 107houses 36%

Gasfurnace 92houses 31%

Inductioncooker 87houses 30%

Solarium 24houses 8% Jacuzzi 8houses 3% Sauna 5houses 2% Waterbed 13houses 4% Aquarium 10houses 3% Terrarium 13houses 4% Close-in-Boiler 28houses 9%

Extraheating 14houses 5%

Ventilator 45houses 15%

AirConditioning 13houses 4%

Videocamera 64houses 21%

Videogames 60houses 21%

Homecinema 80houses 27%

Harddiscrecorder 69houses 23%

Videorecorder 98houses 33%

Otherappliances 33houses 20%

conscious-warm, conscious-cold) according tothe use of

heat-ingandventilationsystems,householdappliances,householdand

dwellingcharacteristics.Shedidnotﬁndstatisticallysigniﬁcant

dif-ferencesbetweenthebehavioralproﬁlesandpatternsintermsof

energyconsumption.

Existing research on behavioral patterns of electricity

con-sumptionfocusonparameters relatedto‘attitude,’ ‘motivation,’

‘lifestyle,’‘householdcomposition,’‘appliancepossession,’

‘house-hold and buildingcharacteristics.’ Methodologically, behavioral

patternsandproﬁlesareproducedeitherusingcontinuousdataon

actualbehavior(forexample[32–34])orbyclusteringbehavioral

proﬁles based oncross-sectional data abouthousehold charac-teristics (for example [12]), and some by combining both (for example [27–29]). In existing research, relationships between behavioral patterns,and household and buildingcharacteristics

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haverarelybeeninvestigated.Ourworkcontributestothe liter-atureby[1]using(partially)continuousdataonactualbehavior as well as household and dwelling characteristics, [2] driving behavioralfactors,patterns,andproﬁles,andlinkingthemtoeach other as well as looking for their relationship with electricity consumption.

Thereareseveralstudiesthatfocusonidentifyingthebehavioral patternsandproﬁlesforheatingenergyconsumption,butnoneon electricityconsumptionbehaviorinDutchhousingstock. Deter-miningbehavioralproﬁlescouldleadtomoreaccurateprediction ofelectricityconsumptionindwellings,betterplanningforthe tar-getedenergysavingmeasures,andhelpingenergycompaniesfor moreprecisecalculations.

3. Methodology

3.1. Researchframeworkandmethods

Inthispaper,wedeﬁnedoccupantbehaviorasthepresenceina space,theuseoflightingandappliances,andtheactivitiesathome thatdirectlycauseelectricityconsumption.Figs.2and3displaythe researchframeworkandmethodology.Westartedwithananalysis oftheapplianceuseinthedatabase.Throughadescriptiveanalysis, wereportedthemaximum,minimumandmeanlevelsof owner-shipanduseofappliancesinthedatabase(Section4.1,Table1). Secondly,weresearchedtheeffectofoccupantbehavioron elec-tricityconsumptioninthedatabase,throughcorrelationanalysis betweenthebehavioral,householdanddwellingcharacteristics, occupantpresence,electricityconsumption(Section4.2,Table3).

Instepthree,weconductedexploratoryfactoranalysisto deter-minethefactorsunderlyingbehaviorofelectricityconsumption (Section4.3,Table4,Fig.4).Behavioralfactorsareclustersof vari-ablesthatconstitutethedriversofbehavior.Followingthefactor analysis,thehouseholdvariablesweredichotomizedaccordingto theirscoresforeachbehavioralfactor(belowthemean=0,above themean=1),whichmeantthateachhouseholdhada‘0’or‘1’score foreachfactor,andeachhouseholdhadastringcomposedof‘0’sor ‘1’s.Categorizingthehouseholdsaccordingtothecommonstrings, thebehavioralpatternsweredeﬁned(Section4.3,Table5,Fig.5).

Instepfour,thebehavioralfactorswereusedincorrelation anal-ysis,inordertofindouttherelationshipbetweenbehavioralfactors andhouseholdanddwellingcharacteristics.Thehouseholdswere distributed intogroups based onthecorrelationoutputs, these groupsweretheuserprofiles(Section4.4,Tables6and7,Fig.6). Lastly, we looked for the relationship between the behavioral factors,patternsandthebehavioral profiles(Section4.5,Fig.7). Following,therelationshipbetweenbehavioralpatterns,profiles andenergyconsumptionwasdetermined(Section4.6,Fig.8). 3.2. Data:explanationofdata,outliers,transformedvariables

Thestudydatawascollectedviaasurveyintwodistricts (Water-ingseVeldandLeidscheRijn)intheNetherlandsonlyintheWinter of2008.Thedatabaseof323casescovered arangeoftopics in theformofaquestionnaire,withregardtohousehold characteris-tics(size,composition,yearsofresidenceinthedwelling,changes inhouseholdcompositioninthepreviousyear),individual char-acteristics(age, education, occupation, hoursspent outside the home),economic characteristics (income,ownership, electricity tariff),presence (numberof people and duration of occupation ineachroom), dwellingcharacteristics(type,number ofrooms, functionofrooms),applianceuse(numberofdomesticappliances, numberofappliancesinthelivingroom,standbyappliances, charg-ers,durationofuse,appliancelabels,sizes),andlightingdevices (number,type).

3.2.1. Outliers

Outlierswereanalyzedandvariablefrequencieswerechecked toseehowmanyofthevariablescouldbeusedforstatistical analy-sis.Outofthe323casesinthedatabase,theelectricityconsumption dataforsevenwereexceptionallyhigh,probablybecausethe occu-pantsdidnotactuallyrecordtheelectricityconsumptioninthe pastyearbutwrotethemeterreading.Twelvequestionnaireswere returnedblank.These19caseswerethereforeexcludedfromthe database,leavingaﬁnalsamplesizeof304.

3.2.2. Missingdata

Someofthedatainthedatabasewereinsufﬁcienttobeincluded inthestatisticalanalysis,hencewerenotincluded,namely: • Thenumberofweekswhennobodyisathome;

• Whethertheelectricityandgasmeterswerecheckedregularly • Appliancelabels

3.2.3. Transformedvariables

The‘electricitytariff’cantaketwovaluesintheNetherlands: [1]singletariffconsumption−onedaytimeandeveningrateon weekdaysandweekends[2],doubletariffconsumption−two dif-ferentrates,oneforduringthedayandanotherforevenings,nights andweekends.Theelectricityconsumptiondataobtainedfromthe surveywerebasedonkWhvalues. Somecaseshadsingletariff consumption records(9%),and somehaddouble records(91%). Toobtainaﬁnalvariableforelectricityconsumption,acheckwas performedtodeterminewhetherasingleordoubleelectricity tar-iffmadeadifference.Nosigniﬁcantcorrelationwasfound,sothe singleandthedoubletariffrecordingswerecomputedtoone elec-tricityconsumptioncategory.

Therespondentsretrospectivelyreportedtheirhourlypresence athomeandindifferentrooms,duringtheweek.Thisdatawas transformedintototalhourlypresenceinroomsduringthe morn-ing,theday,theevening,thenightandallday.

Intermsofthenumberofappliancesowned,andtheduration ofuseoftheappliances,weconductedtwotransformations.First, inordertoobtainatotalﬁgureofdurationofuse,wemultiplied thenumberofappliancesinthehousewiththedurationofuse ofeach.Secondly,weaddedupthetotaldurationofuseof appli-ancesperfunctionofgroup.Wecreated4groupswithfunctions of ‘InformationCommunicationEntertainment (ICE)’, ‘Cleaning’, ‘Foodpreparation’and‘Continuouslyused’appliances(Table1).

Following,theresultsofthestudyarereportedin4Sections: 1.Descriptiveanalysisonappliance ownershipanduse;(2)the impactofoccupantbehavioronelectricityconsumption;(3) behav-ioralfactors,patterns,andproﬁlesofelectricityconsumption;as wellas(4)therelationshipamongthem.

4. Results

4.1. Applianceusebehavior

Themean,maximumandminimumnumberofeachappliance inthesample,andtheirdurationofuse(minutesperday)were reportedandcategorized in4 groups,i.e.‘Information Commu-nicationEntertainment (ICE)’, ‘Cleaning’, ‘Foodpreparation’ and ‘Continuouslyused’appliances(Table1).Onaverage,therewere 21appliancesinahouseand5oftheseapplianceswereinthe liv-ingroom.Theaverageelectricityconsumptioninoursamplewas 3058.57kWh/year.

Onaverage, therewasafridge,afreezer,a wirelessinternet router,andatelephonethatworkedcontinuouslyineachhouse.As forcleaningappliances,adishwasherandadryer,avacuumcleaner andanironwereusedineachhouseinthesample.ICEappliances

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Dwelling characteristics Household characteristics Lifestyles Attitudes Lighting behavior Electricity consumption Determinants of behavior Presence Aplliance use behavior Electricity consumption related behavior Electricity consumption related behavior

Fig.2. Researchframework.

Behavioral variables

Energy consumption Household characteristics

Dwelling characteristics

Correlation St. signific

a nt behavioral variables Factor Analysis Behavioral factors Behavioral patterns Correlation Energy consumption

Correlation Anova Correlation Anova Section 4.2 Section 4.4 Section 4.3 Section 4.6 Section 4.5 Section 4.1 Behavioral profiles

Fig.3. Researchmethodology.

were2TVs,aPC,alaptop,aDVDplayer,andamusicplayer.Lastly, adishwasher,amicrowaveoven,atoaster,agrill,awaterheater, acoffeemaker,andanexhausthoodcreatedthesetoffood prepa-rationappliancespresentineachhouseonaverage,inoursample. Exceptforcontinuouslyused,alltheappliancegroupswesetup refertoaspeciﬁcfunction/activityinthehouse.Besides,only‘food preparation’appliancesisacategorythatrelatetoaspeciﬁcroom (kitchen)inthehouse.

Someofthehousesalsoownedspeciﬁcappliances.The own-ershipand/ortheuseoftheseapplianceswerenothighenough, sowedidnotincludetheminthefactoranalysis.Thenumberof appliancestheypossessedwerereportedinTable2.

4.2. Effectsofoccupantbehavior,householdandbuilding characteristicsonelectricityconsumption

Correlationanalyseswerecarried outtodeterminethe rela-tionshipbetweenoccupantbehaviorandelectricityconsumption (Table3).Thefirstsetofvariablesconsideredweretheuseof house-holdappliances.ICE(Information-Communication-Entertainment) appliancesappearedtohavethemostsignificantinfluenceon

elec-tricityconsumption(r=0.98***),whichwasfollowedbythetotal durationof useofhouseholdcleaning(r=0.13**),food prepara-tion(r=0.09*)andcontinuouslyused(r=0.02*)appliances.Inthe survey,respondentswerealsoaskedtoreporttheirbehavioron theweeklyuseofappliances,andthetotaluseparticularlyinthe livingroom,howeverthesevariables didnot seemtobe corre-latedtoelectricity consumption,hencetheywereomittedfrom theanalysis.

Secondly, the influence of the use of stand-by and battery charged appliances, and the ownership of energy saving, non-energy saving lamps, and PV/solar panels were analyzed. The mostsignificantimpactonelectricityconsumptionwasbyhalogen lamps(r=0.17**).Theuseofbatterycharged(r=0.22*),and stand-by (r=0.15*) appliances had a positive influence on electricity consumption,whileenergysavinglamps(r=−0.04*),andPV/solar panelshadanegativeone.TheownershipofPV/solarpanelsdidnot, infact,significantlycorrelatewithelectricityconsumption, how-everthisparameterwasincludedinthefactoranalysis,tosetup behavioralpatternsandprofiles.

Theuseofmechanicalventilationwasnotfoundtobecorrelated withelectricityconsumption, buttheuseofshower(r=0.23**),

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Fig.4.Behavioralfactorsandthevariablesthatdeterminethesefactors.

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Table3

Descriptiveandcorrelationanalysisofhouseholdanddwellingcharacteristics,occupantbehaviorandelectricityconsumption.

Variable Deﬁnition Numberof

cases Meanand Standard Deviation Correlation Electricity Consumption

Householdappliances Continuouslyused Totaldailydurationofuseofcontinuouslyusedappliances H:118 M:4895.58 0.02*

L:164 SD:2414.45 N:282 Foodpreparation Totaldailydurationofuseoffoodpreparationappliances H:107 M:238.77 0.09*

L:175 SD:176.26 N:282 Householdcleaning Totaldailydurationofuseofhouseholdcleaning

appliances

H:99 M:116.98 0.13** L:183 SD:105.88 N:282 ICE TotaldailydurationofuseofICEappliances H:89 M:1457.92 0.98*** L:193 SD:1376.59 N:282 Stand-by Totalnumberofstand-bymodeofappliances H:120 M:2.75 0.15*

L:174 SD:3.06 N:294 Batterycharged Totaldurationofbatterychargedappliances H:65 M:67.5 0.22*

L:239 SD:140.11 N:304 Energysavinglamps Numberofenergysavinglamps H:104 M:5.89 −0.04* L:190 SD:6.05 N:294 Halogenlamps Numberofhalogenlamps H:117 M:14.52 0.17**

L:177 SD:10.07 N:294 PV/Solarpanel PresenceofPVorsolarpanels Y:46 M:0.15 −0.79(r:0.23)

N:248 SD:0.36 N:294 Hotwashcycles Totalweeklynumberhotlaundrycycles H:62 M:0.94 0.19**

L:230 SD:1.50 N:292 Showers Totalweeklydurationofshowersinthehousehold H:122 M:139.21 0.23**

L:182 SD:135.28 N:304 Bath Totalweeklynumberofbathsinthehousehold H:90 M:1.33 0.14*

L:214 SD:2.59 N:304 Presence Room1 Totalhoursofpresenceinroom1(weekdays/allday) H:167 M:13.61 0.22*

L:109 SD:5.35 N:294 Room2 Totalhoursofpresenceinroom2(weekdays/allday) H:111 M:5.18 0.31*

L:165 SD:4.08 N:294 Room3 Totalhoursofpresenceinroom3(weekdays/duringthe

day)

H:20 M:0.97 0.12* L:259 SD:0.20 N:294 Livingroom-Kitchen Totalhoursofpresenceinlivingroom-kitchen

(weekdays/morning)

H:85 M:2.52 0.21** L:188 SD:2.11 N:294 Bathroom Totalhoursofpresenceinbathroom(weekdays/morning) H:91 M:1.28 0.18**

L:182 SD:1.17 N:294 Household

characteristics

Householdsize Householdsize H:115 M:2.53 0.38**

L:183 SD:1.17 N:301 Yearsofresidence Yearsofresidenceinthesamehouse H:151 M:5.38 −0.16* L:136 SD:3.13 N:287 Age Presenceofagegroup6–65inthehousehold Y:214 M:3.00 −0.72* N:84 SD:0.75 N:298

Income Monthlyhouseholdincome H:171 M:3.99 0.13*

L:113 SD:1.04 N:284 Education Amemberofthehouseholdhasuniversityorhigher

education

Y:32 M:5.46 −0.03(r:0.22) N:270 SD:2.03 N:302 Workingoutside Hoursspentoutsidethehouse H:178 M:23.60 0.97(r:0.13)

L:124 SD:14.03 N:302 Dwel.C. Dwellingtype Typeofdwelling(corner/self-standinghouse,topﬂoor

apartm.)

Y:46 M:2.95 −0.23* N:255 SD:1.05 N:301

Bedrooms Numberofbedrooms H:85 M:1.84 0.26**

L:218 SD:0.97 N:303 Notesoncasesandabbreviations:

H:Numberofcasesthathavehighervaluethanthemeanvalue. L:Numberofcasesthathavelowervaluethanthemeanvalue. Y:Numberofcasesthathavepositiveresponsetothequestion. N:Numberofcasesthathavenegativeresponsetothequestion. Householdincome:Hmeanshigher(LforLower)than56000Euros.

Age:Meanvalueofagegroupsinthesampleis“16–65yearsold.”However,forcategorizinghouseholdsintermsofelectricityconsumption,weexpandedthegroupto(1) ‘6–65yearsold;’and(2)‘childrenandelderly.’

Dwellingtype:Themeanvalueof2.95meansrowhouseisthecommontypology.Forcategorizinghouseholdsintermsofelectricityconsumptioninouranalysis,we re-categorizedthisvariableaccordingtohowmuchthedwellingmightbereceivingdaylight.Thus,wecreatedtwogroups(1)corner,orself-standinghouses,ortopﬂoor ﬂats;and(2)rowhouse,orgroundormiddlelevelhouses.

*_p_<_0.05. **_p_<_0.01. ***_p_<_0.001.

bath(r=0.14*)andthenumberofhotlaundrycycles(r=0.19**) were.Showerswerecalculatedintermsofthetotaldurationof showersperweekin thehousehold,andbathin termsof total numberofthemperweekinthehousehold.

Presenceinrooms(otherthanthelivingroom)werepositively correlatedwithelectricityconsumption.Thecorrelationanalysis showedthatthepresenceinroom1(r=0.22*)androom2(r=0.31*) allday,room3(r=0.12*)duringtheday,andlivingroom/kitchen

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Table4

Factorscoresandcommunalities(principlecomponentsanalysis).

Variables Components’factorscores Communalities

Factor1 Factor2 Factor3 Factor4 Factor5

Continuouslyused 0.588 0.677 Foodpreparation 0.509 0.527 Cleaning 0.468 0.645 ICE 0.721 0.631 Stand-by 0.493 0.525 Batterychargers 0.624 0.676

Energysavinglamps 0.429 0.704

Halogenlamps 0.530 0.754

PV/Solarpanel 0.515 0.552

Hotwashcycles 0.448 0.755

Dryer 0.522 0.742 Dishwasher 0.562 0.677 Showers 0.577 0.325 0.695 Bath 0.432 0.589 Room1 0.487 0.491 Room2 0.660 0.573 Room3 0.406 0.602 Livingroom-Kitchen 0.617 0.605 Bathroom 0.657 0.617

Rotationmethod:VarimaxwithKaiserNormalization(formoreexplanationontherotationmethod,seereference35).

Factorscores<0.4aresuppressed.

Table5

Distributionsofcases(N)andstringsaccordingtofactors,andDerivationofbehavioralpatterns.

Nameofpattern Factor1Total

applianceuse Factor2 Articulationof technology Factor3Spatial Presence Factor4 (Personal) Cleaning Factor5Energy conservation Numberofcases thatconstitutea string 1.Applianceuse 111 101 010 101 100 212423 2.Presence/Technology 1111 1111 1100 1010 0100 25222621

3.Presence/(Personal)Cleaning 1111 1010 1111 1111 0100 19231822

4.Energyconservation 11 01 01 00 11 1820

Table6

Correlationsbetweenhouseholdanddwellingcharacteristicsandbehavioralfactors.

Householdanddwellingcharacteristics FactorScore1 FactorScore2 FactorScore3 FactorScore4 FactorScore5

Total applianceuse Articulationof technology Presence (Personal) Cleaning Energy Conservation

Dwellingtype(corner/free-stand/topﬂoor) PearsonCorrelation −0.18 −0.07 – −0.03 −0.04

Signiﬁcance(2-tailed) 0.03 0.38 – 0.05 0.05

Numberofbedrooms(otherthanlivingroom) PearsonCorrelation −0.17 0.31 – 0.08 0.10

Yearsofresidenceinthesamehouse PearsonCorrelation 0.01 −0.03 – 0.00 0.03

Householdsize PearsonCorrelation −0.16 0.36 – 0.17 −0.11

Presenceofchildrenorelderly PearsonCorrelation 0.13 −0.19 – 0.14 0.04

Educationlevel(highestlevelinthehousehold) PearsonCorrelation −0.01 0.01 – −0.10 −0.03

Hoursspentoutsidethehouseforwork PearsonCorrelation 0.09 0.10 – 0.08 −0.05

Incomelevel PearsonCorrelation −0.50 0.11 – 0.09 −0.01

Table7

Behavioralfactorsandbehavioralproﬁles.

Factor NameofFactor CorrelatedHousehold/Dwellingvariable

Factor1 Totalapplianceuse -(Oldercouple)-Middle-groundﬂoordwelling-Lowerincome-Moreworkoutside-Householdsize(<2)

Factor2 Articulationoftechnology -Numberofbedrooms-Workathome-Higherincome-Householdsize(=>2)

Factor3 Spatialpresence –

Factor4 (Personal)Cleaning -Numberofbedrooms(>2)-Householdsize(>2)-Workathome-Higherincome-Younghousehold

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Fig.6.Household/dwellingcharacteristics,behavioralfactors,andproﬁles.

(r=0.21**)andbathroom(r=0.18**)inthemorningwerepositively

andsigniﬁcantlycorrelatedwithelectricityconsumption.

4.3. Behavioralfactorsandpatterns

Afactorcanbedescribedwithitsmeasuredvariablesandtheir

relativeimportancetothatfactor[35].Therelationshipamong

dif-ferentvariablesinadatabasecanbedescribedusingfactoranalysis, byexploringthefactorsthathelptoidentifytherelatedbehaviors. Weusedexploratoryfactoranalysistoidentifybehavioralfactors underlyingelectricity consumption. We used thevariables that weresigniﬁcantlycorrelatedtoelectricityconsumption(Table3). However,someofthevariablesthatwerenotsigniﬁcantly corre-latedtoelectricityconsumptionwerestillincludedintheanalysis, consideringthattheymight revealfurtheraboutthebehavioral patterns.

Accordingly, 19 variables were used for the factor analysis. Tostartwith,wecheckedifthefactor analysiswassuitablefor oursample:Thecorrelationsignificance andthecoefficient val-ueswerecheckedbetweenthedifferentvariables.Majorityofthe significancevaluesweresmallerthan0.05andcoefficientvalues werelowerthan0.9,whichmeantthattherewasreasonable fac-torability,hencenoneofthevariableswereeliminatedfromthe analysis.Thedeterminantvaluewas0.00239,whichwasgreater than0.00001,thereforemulticollinearitywasnotaproblemfor thedata.Next,theKaiser-Meyer-Olkin(KMO)measureofsampling adequacy,andBartlett’stestofsphericitywerecontrolled.TheKMO valuewas0.73,andBartlett’stestwashighlysignificant(p<0.000) showingthatfactoranalysiswasappropriatetoanalyzeour sam-ple.Oursamplesizewasgreaterthan250, wehadlessthan30

variables,andmostoftheircommunalitiesafterextractionwere around0.7,aswellastheiraveragecommunalitywas0.67(which wasgreaterthan0.6),thereforeweretainedallfactorsthathave Eigenvaluesabove1(See[35]foradeﬁnition,andmore explana-tiononKMOmeasure,Bartlett’stestofsphericity,andEigenvalue infactoranalysis).

Basedoneachvariable’sprimaryscoreoneachfactor,thefactor scoreswerecreatedforthefactors.Table4displayedtheanalysis resultsintermsofthevariablesdefiningeachofthefivefactors, aswellasthefactorloadingmatrixandtheircommunalities.The initialEigenvalues,i.e.degreeofvariationinthetotalsample cre-atedbyeachfactor,displayedthatthefirstfactorexplained16.29% ofthevarianceinelectricityconsumption,thesecond15.23%,the third13.79%,thefourth9.00%,andthefifth7.84%,creatinga cumu-lativeof62.15%.Factors6–19wereabletoexplainaround3–4%of thevarianceeach.Accordingly,thefirst5factorswerechosento usefurtherinthestudy.Thesefactorswerenamedas:‘total appli-anceuse,’‘articulationoftechnology,’‘spatialpresence,’‘(personal) cleaningbehavior’and‘energyconservation’(Fig.4).

Accordingly,Factor1 wasmerelyaboutthetotaldurationof applianceuseinthedwellingandcomprisedofthecontinuously used,foodpreparation,andcleaningappliances.Factor2wasabout theuseofInformation, CommunicationandEntertainment(ICE) appliances,andtheuseofstandbyandbatterychargedappliances. Thisfactorimpliedamoretechnologyanddeviceorientedlifestyle, aswellashome-ofﬁce workingpreferences.Factor3related to thepresenceoftheoccupantsintherooms,inthekitchen/living roomandthebathroom,andtheintensiveuseofhalogenlamps. Factor3pointedtotherelationshipbetweenspatialuseathome andelectricityconsumption.Halogenlampsemphasizedtheless

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Fig.7.Relationshipsfoundbetweenhousehold/dwellingcharacteristics,behavioralfactors,patterns,andproﬁles.

Notes:Outersquare/Edges=behavioralpatterns.Centerpentagon/Edges=behavioralfactors.Innersquare/Edges=behavioralproﬁles.Lines=household/dwelling charac-teristics(tothebottomandleftcharacteristicsthatarerelatedwithlesselectricityconsumption;tothetopandrightcharacteristicsthatarerelatedwithmoreelectricity consumptionaredistributed.)

2000 2500 3000 3500 4000 4500

appliance presence/ presence/ energy use technology [personal] cleaning conservation kWh/year 2000 2500 3000 3500 4000 4500 kWh/year

family techie comforty conscious

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energyconsciousattitudeagainsteverydaylife.Factor4relatedto theintensivelaundryandpersonalcleaninghabits.Thenumberof hotwashes,theuseofdryeranddishwasher,aswellasthe dura-tionofshowers,andthenumberofbathspointtothesignificanceof theinfluenceofcleaninghabitsonelectricityconsumption.Factor 3and4alsohintedattherelationshipbetweenoccupantcomfort andelectricityconsumption.Factor5relatedtolessuseof elec-tricity.Thevariablesthatdefinedthisfactorweretheownership ofPV/solarpanels,energysavinglamps,and thelaundryhabits, wheretheownershipofPV/solarpanels,energysavinglamps,as wellasthedecreasingnumberofdryerandhotwashingcycleshad anegativeinfluenceonelectricityconsumption.

Todeterminethebehavioralpatterns,firstwedichotomizedthe factorscoresofthecasesinoursample.Wedidthisbycomparing eachcase’sfactorscoretothesample’smeanfactorscoreobtained fromthefactoranalysis(ifabove=1,ifbelow=0).Thenwerepeated itforthefivefactors.Throughthis,thefivedichotomousscoresfor eachcaseinthesample,i.e.eachhousehold,createdastring.The clusteringofallstringsrevealedthirteencategories(Table5).

Afterwards,thesecategorieswereclusteredoncemore, accord-ing to the correlation between the behavioral variables that composethefactorsandelectricityconsumption(seeTable3for thecorrelationanalysis). Eventually,thirteenstringswere orga-nizedinto4patterns(Fig.5):Pattern1:(Applianceuse),Pattern 2:(Presence/Technologyoriented),Pattern3:(Presence/Comfort oriented),Pattern4:(Energyconservation).Table5 showedthe behavioralpatterns,thefactors,andthedistributionsofthestrings foreachbehavioralpatternandfactor.

4.4. Behavioralfactorsandproﬁles:Householdandbuilding characteristicsrelatedtobehavioralfactors

In ordertodeterminethe behavioral profilesin thesample, weanalyzedthebehavioralfactorsintermsoftheircorrelationto thehouseholdandbuildingcharacteristics.(Table6).Wesawthat spatialpresencewasnotattachedtoacertainhouseholdand/or dwellingcharacteristic,howeveritcomplementedprofile2and3. AnalyzingTable6,wefoundthehouseholdprofilesof‘family,’ ‘techie,’‘comforty,’and‘conscious,’whichwereexplainedfurther withinthedescriptionsoftheprofilesinthenextparagraphs,and inTable7,Fig.6.

Theresultsshowedthatthehouseholdsthathadhigh correla-tionvaluesforfactor1:‘applianceuse’weremostlyyoungcouples, except thefew cases of the elderly.These households had the averagebehavior,bothintermsofownershipandusageof contin-uouslyused,foodpreparationandcleaningappliances.Theylived ongroundormiddlefloorapartmentorrowhouse,whichinfluence thenaturallightlevelinthehouse(hencetheelectricity consump-tion).The householdshad slightlylower incomeinsomecases, comparedtotheotherprofiles.Wecalledthisprofileas‘family.’

Thehouseholdvariablesthatrelatedtofactor2:‘articulationof technology’hadhighereducationlevel,higherincomelevel,and insomecases,lowerhoursofworkingoutside.Variablesrelatedto householdcompositiondidnotappearcorrelatedwiththisfactor, butthisprofilehadyoungsingleorcouplehousehold.Oneorboth membersofthehouseholdprobablyhadaflexibleworking sched-ule,andpossiblyfreelancingand/orworkingathome.Thehigher educationandlesshoursofworkingoutsidewaspotentiallyrelated tothehigheruseofICEappliances,stand-byandbatterycharged appliances.Thishouseholdtypewasalsorelatedtofactor3‘spatial presence,’i.e.bedroom3(label3referstotheextrabedroom,or extrafunctionofthebedroomotherthansleeping)andbathroom. Theuseofbedroom3duringthedayconfirmedworkingathome orhome-officeconfiguration.Theuseofbathroominthemorning mightberelatedtoshowerandotherpersonalcleaningbehavior, howeverthefactorof‘personalcleaning’wasnotfoundcorrelated

withthisproﬁle.Wenamedthisproﬁleas‘techie.’Thisgroupalso hadthelargestnumberofharddiscrecorders,videocamerasand videorecorders,whichwerenotincludedintheanalysisbecause oftheirsmallamountinthesample.

ThevariableswhichwererelatedtoFactor4((personal)cleaning behavior),weredwellingtypology(cornerorfreestanding), num-berofbedrooms,andahouseholdproﬁleofhigherincomelevel, biggerhouseholdsize,andlesshoursofworkingoutside.Thisgroup livedinlargerhouseswithmorethanonebedroom,oneormore children,andpossiblyoneoftheparentsorbothparents-parttime stayedathome.Thisgroupcameforwardwithitsintensiveuseof appliancesthatrelatedtodwellingand/orhouseholdcleaning,i.e. durationofshowers,numberofbaths,dishwasheruse,numberof hotlaundrycyclesanddryerloads.InadditiontoFactor4,thisgroup wasalsorelatedtoFactor3,presenceinbedroom1and2,which complementedthecorrelationwiththevariablesofthenumber ofbedroomsandworkinglesshoursoutside,andpresencein liv-ingroomandkitchen.Thisgroupalsousedmorehalogenlamps, whichpointstolessinterestinenergysaving.Wenamedthisgroup ‘comforty.’Thisgrouphadthelargestownershipofinductionand electricitycooker,waterbedandairconditioning,videogamesand homecinema,whichwerenotnormallyincludedintheanalysis becauseoftheirrelativelysmallnumberintheentiresample.

ThishouseholdprofilerelatedtoFactor5‘energyconservers,’ whichmeantmoreuseofenergysavinglamps,andownershipofPV and/orsolarpanels,howevertheseparametersdidnotappear sig-nificantlycorrelatedwiththefactor.Thehouseholdprofilehadless useofshowercomparedtootherprofiles,anditusedlessofdryer andhotlaundrycycles,whichrelatedtoFactor4‘(personal) clean-ingbehavior.’Thishouseholdprofilehadhighereducationlevel, workedmorehoursoutsidethehouse,hadsmallerhouseholdsize, andlivedintopfloorapartmentorcornerhouseinsomecases.The profiledidnotincludeasignificantlycorrelatedincomeparameter, butithadmoreincomethanprofile‘family,’andlessincomethan profile‘techie’and‘comforty.’Wecalledthisgroupas‘conscious.’

4.5. Relationshipsbetweenbehavioralpatterns,proﬁles,and factors

Fig.7showedhowthebehavioralfactors,patterns,proﬁles,and characteristicswererelatedtoeachother.Thebehavioralpatterns formedtheouterlayer,thebehavioralfactorsformedthemiddle pentagon,andthebehavioralproﬁlestheinnersquare.Theouter squarerepresentedthebehavioralpatterns.Astopandrightmeant moreuseofelectricity,theleftandbottommeantlessuseof elec-tricity.Themiddle pentagonshowedthebehavioral factors,i.e. total applianceuse,articulation oftechnology, (personal) clean-ing,andenergyconservation.Thebehavioralpatternsandfactors seemedtobeconsistent,exceptforthefactor‘presence,’which appearedbothwithin(personal)cleaningandtechnologypatterns. Whenelectricityconsumptionandunderlyingbehavioralfactors areconsidered,thepatternsof‘presence/technology’and‘energy conservation’seemedtooppose,aswellas’(personal)cleaning’ and‘useofappliances.’

Householdproﬁlesof‘conscious’and‘techie’seemedtooppose, when thehouseholdand dwellingcharacteristicsrelated tothe behavioralfactorsweretakenintoaccount.Forinstance,conservers worked more hours outside compared to techies, and seemed tolive in dwellingsthat get moreday light. Techieshad more householdincome.Bothgroupshadhigheducation,althoughonly forconserversthisvariablewassigniﬁcantlycorrelatedwiththe behavioralfactors.Similarly,‘comforty’and‘family’opposedwith eachother.‘Comforty’wasofyoungerhouseholds,whohadhigher incomeandhighernumberofchildren,spentmoretimeathome

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andhadbiggerhouses.‘Family’wasolder,smallerinhouseholdsize andincome,andspentlesshoursathomeingeneral.

4.6. Relationshipsbetweenbehavioralpatterns,behavioral proﬁles,andelectricityuse

Thecorrelationanalysisbetweenbehavioralfactorsand elec-tricityconsumption revealed that factor 1 (appliance use) was correlated with electricity consumption r=0.11, p<0.05; factor 2(articulationoftechnology)byr=0.35,p<0.00;factor3 (pres-ence)wasnotsignificantlycorrelatedwithelectricityconsumption (r=0.14,p<0.15);factor4(personal)cleaningbyr=0.37,p<0.00; andfactor5(energyconservation)wassignificantlycorrelatedwith electricityconsumption(r=0.13,p<0.05).Thesefactorswereused todefinebehavioralpatterns.

For determining the differences in electricity consumption for each behavioral pattern, we conducted a one-way Anova test,wherewefoundstatisticallysignificantdifferences(r=0.17, p=0.02).Boththestatisticallysignificantdifferencesamong behav-ioralpatterns,andthesimilaritiesbetweenourresultswiththose oftheliteratureshowedthatourresearchmightbeusedfurtherfor researchonelectricityconsumptionandoccupantbehavior.Fig.8 showedtheenergyconsumptionforeachbehavioralpattern(left). Following,welookedatthebehavioralprofilesinrelationto electricityconsumption(Fig.8(right)).‘Family’hadahighscore forapplianceuse,‘techie’(technologyorientedsingles/coupleswho alsoworkedathome)hadahighscoreforarticulationoftechnology andpresence,‘comforty’(largefamilieswithhighpreferencefor comfort,showers,baths,dryer,etc.)hadahighscoreforpresence and(personal)cleaning,and‘conscious’(singlesorcoupleswith higheducationandworkingoutside)forenergyconservation(PVs, energysavinglamps,etc.).Wefoundstatisticallysignificant differ-encesamongthefourprofilesintermsofelectricityconsumption (r=0.19,p=0.02).

5. Discussion

In this paper, we aimed to analyzein detail thebehavioral aspectsofhouseholdelectricityconsumptionintheNetherlands. Inthissection,wepresentadiscussion[1]ontheappliance own-ership,useanddailylife;[2]ontheresultsoffactoranalysis,i.e. thebehavioralfactors,patternsandproﬁles,andtheirrelationship withelectricityconsumption;[3]onthecomparisonofourresults withtheexistingresearch;and[4]onmethodology.

5.1. Applianceownership,useanddailylife

Intermsofownershipofappliances,everyhouseholdowning adryer,aseparatefreezer,and6batterychargedappliancesisa remarkableresult.Presenceinrooms/athometellsusaboutthe timesofthedaythattheappliancesareused.Ingeneral,itcouldbe saidthatmostappliances,exceptforICEareusedinthemorning (07:00–09:00),andtheevening(18:00–20:00).

Inoursample,everyhouseholdhasonaverage2TVs,1desktop computer,1laptop,1stereosystemand1DVDplayer.Some house-holdshave1TVand1laptopperperson.Thetotaldailyhoursspent watchingTVis4honaverage,PCuseperdayisapproximately2and ahalfhours,andlaptopuse3h.ThissuggestshowcentralTVsand computersaretoourlives.TVsarethemostimportantelectricity consumersathome,theenergyefﬁciencyofwhichhaven’tbeen improvedaswellastheotherappliances.Whenwethinkofthis togetherwiththenumberofbatterychargedappliances,wecould saythepossessionanduseofICEapplianceswillbeveryimportant forpolicyeffortsinreducingelectricityconsumptioninfuture.

Asforcleaningappliances,adryerisused2timesperweekand awashingmachine5times.Thesenumbersshowthatalmostevery

itemofclothingiswornonlyoncebeforeitiswashed.Whenthis isconsideredtogetherwiththe17minuseoftheironperdayand theonceortwiceshowersperpersonperday,ittellsusaboutthe occupationsand/ortheintensecleaningandcomfortpreferences ofthehouseholds.

Intermsoffoodpreparationappliancesperhousehold(on aver-age), thefact that there is a freezer in continuous usetells us aboutfoodstoring/eatinghabits.Perhapslessfreshfoodisbeing consumedand/orhouseholdsmightalwaysbepreservingfoodfor winter/summer.Thegrillandmicrowaveovenbeingused24min in total per day suggeststhat themain meals consist of easy-to-preparefood.Lastly,adishwasherisused42minperdayon average,whichmeansthateitherthedishwasherisusedonthe quickcycleeveryday,orthelongcyclenearly4timesaweek. 5.2. BehavioralFactors/Patterns/Proﬁles

Using exploratory factor analysis, we found the behavioral factorsastotalappliance use,articulation oftechnology,spatial presence,(personal)cleaningbehavior,andenergyconservation.In consistencewiththebehavioralfactorswefoundthe4behavioral patternsas theuseof appliances,presence/(personalcleaning), presence/technology,energyconservation.Following,the house-holdanddwellingcharacteristicswereincludedintheanalysis,and thebehavioralproﬁleswererevealedas‘family’,‘techie’,‘comforty’, and‘consciouss’.

Here we saw that the behavioral factor of spatial presence appearedintwobehavioralpatterns,i.e.cleaningandtechnology. WhiletheuseofICEappliancescreatedenoughfactorscoretorelate toaseparatebehavioralfactorandpattern,thebehavioralfactor ofpresenceappearedintwodifferentbehavioralpatterns ((per-sonal)cleaningandtechnology).Thepositiveornegativebehaviors of(personal)cleaninganduseofhalogenorenergysavinglights alsoleadtotwodifferentpatterns((personal)cleaningandenergy conservation).

Bydeﬁninghouseholdcharacteristicsinrelationtobehavioral factors,andtherelationshipbetweenbehavioralfactorsand pat-terns,onecoulddeterminetheassociatedbehavioralfactorsand behavioralpatternsofa household.Forinstance,ifahousehold is partof the‘techie’ proﬁle,we couldexpect a highscore for ‘articulationoftechnology’and‘presenceathome,’whichmeans working/beingpresenthighhoursintherooms,and usinga lot oftechnologicaldevices,includingICEappliances,stand-by,and batterychargedappliances.

Thehigherorlowervaluesofhouseholdsize,income,education, workingoutside,number ofbedrooms, anddwelling typewere foundtoberelatedtodifferentbehavioralfactors.Forinstance, the‘comforty’profilehadbiggerhouseholdsize,higherincomeand numberofbedroomscomparedto‘family,’whileithadlower work-ingoutsidehours.The‘conscious’profilewasfoundtohavemore hoursofworkingoutside,smallerhouseholdsize,andhigher edu-cation,comparedto‘techie,’andwasfoundtoliveinahousethat getsmoredaylight.Theprofile‘conscious’didn’tnecessarily cor-relatetoincome,butithadmoreincomethanprofile‘family,’less incomethan‘comforty.’Inoursample,consideringtheelectricity consumption,thebehavioral profilesdidnotrelatetoparticular householdstereotypessuchassingle,couple,elderly,etc.,butto variablessuchasworkinghours,householdsize,education,and income.

5.3. Comparisonwithliterature

OurresultsweresimilartothoseofWidenetal.[28]: Electric-ityconsumptioniscloselyrelatedtooccupants’presence.Besides, applianceusebasedonspeciﬁcactivitieslikecooking,washing, lighting,TVandPCusecouldbeagoodwaytomodeloccupant

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behaviorandelectricityconsumption,andtherelatedprofiles.In ourresearch,wefoundthattheuseofICEappliances(articulation oftechnology)determinedabehavioralpatternonitsown. Cole-manetal.’sresearch[29]alsopointedtothesignificanceofICE appliances:“computersandTVsduringtheactivepowermode, andaudioappliances,printers,andotherplayandrecord equip-mentduringstandbyconsumptionaresignificantend-users(23%of electricityconsumption). ÄccordingtoO’Dohertyetal.[30] house-holdersundertheageof40hadthemostappliancesbutalsothe mostenergysavingappliances(ESA).Inoursample,thetwogroups hadthemostnumberofapplianceswereyoungsingles,couplesor families,whichcompliedwiththeresultsofO’Dohertyetal.Lastly, Genjoet al.’s[31]analysisfoundthateconomicaffluencehad a stronginfluenceingroupingthehouseholdsaccordingto electric-ityconsumption.Incomewasoneofthehouseholdcharacteristics thatweusedtodeterminethebehavioralprofiles,aswell.

IntheNetherlands,theresearchonbehavioralprofiles regard-ingenergy consumption focusonheating energy,but still they areinsightfultocomparetoourworkintermsoftheirfindings. RaaijandVerhallen[10]identified5profilesofenergybehavior asconservers,spenders,cool,warmandaverage,andtherelated householdcharacteristicsashouseholdsize,education,and age. Grootetal.andPaauwetal.[16,17]developed4behavioral pro-filesbasedoncomfort,interestinenergysavings,andawareness ofeconomy.Vringer[23]groupedhouseholdsintheNetherlands accordingtoincome, age,educationandhousehold size.Lastly, GuerraSantin’sresearch[12]revealed5groupsaccordingtotheuse ofheatingandventilationsystems,householdappliances, house-holdanddwellingcharacteristics.Thevariablesofhouseholdsize, education,age,comfort,andincomewerealsothosethatweused insettingupthebehavioralprofilesinoursample.Wedidn’tlook intobehavioralattitudeslikeinterestinenergysavingorawareness ofeconomy.Intermsoftheprofilesdefined,‘conservers,’‘family,’ and‘comforty’arethebehavioralprofilesfoundinliterature,and visibleinourresults,aswell.Itmightbeinterestingtolookdeeper intotheseprofiles,sincetheymightrevealmoreaboutthecommon underlyingaspectsofbehaviorthatrelatetosimilarelectricityand heatingenergyconsumptionbehaviors.

5.4. Methodology

Technologicaladvancesanddecreasinghardwarepricesenable newresearchtoutilizesmartmetersandothercontinuousdata collectionmethods (for instance [32–34]).Research that works withthiskindofdatausesanalysistoolslikeproﬁlerecognition (forinstance[27]), timeuseanalysisandloadmodeling[28,36], eigendecomposition(for instance[37])andMarkovchains(for instance[38]).Ourresearchemployeddatacollectedbya question-naire,thereforemostofthedataiscross-sectional,exceptforthe behavioraldata(presence,useofappliancesandsystems)thatwas collectedbasedonaweeklycalendar.Inthiskindofmethodology, collectedcross-sectionaldataonbehaviorismodelledbytoolslike cluster(basedoncases)andfactoranalysis(basedonvariables). Inthisresearch,weworkedwithfactoranalysis.Furtherresearch couldcombinethesetwomethodologies,conﬁrmingeachother’s results,aswellasprovidingmoreinsightintooccupantbehavior andelectricityconsumptionrelationship.

Intermsofthelimitationsofthisresearch,becauseourdatais collectedwithaquestionnaire,evenifthequestionsonpresence andbehavioraredetailedonaweeklybasis,respondentsmight haveﬁlledintheinformationbasedonrememberingtheirhabits, butnot actualbehavior. Thiscouldbediscussed asalimitation ontheonehand,andasasuccessfulapproachontheotherhand [24–26].Secondly,ourdataiscollectedfromtwoVenex neighbor-hoods(satellitetowns)intheNetherlands,whereeducationand economicallevelsofhouseholdsarequitehomogenous.Evenifthe

representationofthesecharacteristicsinoursampleisinlinewith theDutchaverages,thehomogenousdistributionofthevariables bethereasonforthemtocomeupasnot-significantdeterminants ofoccupantbehavior.Thirdly,theinfluenceofHawthorneeffect [39]mustbementioned,wherethesurveyrespondents’ aware-nessofthegoalofthesurveymighthavedirectedthemtofill-in thequestionnairedifferentthanthereality.

6. Conclusionsandfuturework

Thisresearchaimedtoanalyzeindetailtheapplianceuseinthe Dutchhousingstock,anddeﬁnebehavioralpatternsandproﬁlesof electricityconsumption.Weanalyzedsurveydatacollectedfrom 323dwellingsintheNetherlandsonapplianceownershipanduse; presence;cleaning;householdanddwellingcharacteristics.

First, a descriptive analysis was conducted onthe variables relatedtoownershipofappliances,theiruse,presence,and house-holdanddwellingcharacteristics,andelectricityconsumption.We created4groupswith‘ICE’,‘Cleaning’,‘Foodpreparation’and ‘Con-tinuouslyused’appliances.Asasecondstep,correlationanalysis wasconductedtoseetherelationshipbetweenvariablesrelated tooccupantbehaviorandelectricityconsumption.Theoutputsof thisanalysiswereusedtorealizeafactoranalysisrevealingthe underlyingfactorsofbehavior.Accordingly,wefoundtotal appli-anceuse,articulationoftechnology,presence,(personal)cleaning, andenergyconservationasthebehavioralfactorsofelectricity con-sumption.Afterwards,basedonthebehavioralfactors,wedefined thebehavioralpatterns(applianceuse,technology/presence, (per-sonal)cleaning/presence,energyconservation).Lastly,welooked for correlations betweenbehavioral factorsand household,and dwellingcharacteristics,fromwhichwefoundthebehavioral pro-files (family,techie, comforty, conscious). In the nextstep, we consideredtherelationshipbetweenbehavioralfactors,patterns, profilesandelectricityconsumption.Wefoundstatistically signif-icantcorrelationsbetweendifferentbehavioralpatterns,aswell asbetweendifferentbehavioral profilesinrelationtoelectricity consumption.

IntheNetherlands,relationshipsbetweenbehavioralpatterns, householdandbuildingcharacteristicsinrelationtoelectricity con-sumptionhave hardly beeninvestigated.Our workadds tothe researchbyusingactualbehaviordataaswellashouseholdand dwelling characteristics,and by drivingbehavioral factors, pat-terns,andproﬁles,andlinkingthemtoeachotheraswellaslooking fortheirrelationshipwithelectricityconsumption.

Determining behavioral proﬁles could lead to more accu-rateprediction of electricity consumption in dwellings,as well as planning the targeted energy saving measures, and helping energycompaniesforbettercalculations.Consideringthat occu-pantbehaviormightbemorevisibleinthenewerdwellings,and thatbehaviormightberevealedmorepreciselybyanalyzing ‘elec-tricity’consumption,this researchmightprovide moredetailed andarticulatedinputonoccupantbehaviortoresearchandpolicy, which focusonmotivating/encouraging individuals’and house-holds’towardsmoreenergyefﬁcientbehavior.

Intermsoffuturework,wecouldthinkofthefollowing direc-tions:

-Everyhouseholdowning1wirelessinternetrouterincontinuous useand6batterychargedappliancesshouldberesearched fur-therintermsofamobile24/7lifestyleandtheaddictiontobeing ‘connected’.

-Existingstudiesshowedthatlargepartofhouseholdenergyuse canbeexplainedthroughrepeateddailyroutines.Asfollowup work,thecausesofdailyroutinesofbehaviorthatarerelatedto electricityconsumptionshouldberesearchedfurther.

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-Inrelationtothepointabove,collectingandanalyzing longitu-dinaldataonbehaviorisnecessarytoconﬁrmtheﬁndingsfrom cross-sectionaldatatoovercomemethodologicallimitations. -Personalcleaningbehaviorappearedtobeanimportantfactor

bothinthepatternsandproﬁlesinthisresearch,whichsuggestsa comfortrelatedaspectofenergyconsumption.Thisaspectneeds tobeinvestigatedintermsofthemotivations,frequencies,and consequencesoftheparticularbehavior.

-Furtherresearchisalsoneededontheactualhouseholdappliance inventory,theirpowersandenergyratingsinmuchlarger sam-ples.Thisresearchcouldbeextendedbyspeciﬁcallyinvestigating theuseofICEappliances,foodpreparation(especiallyfreezer, dishwasher)and (personal)cleaning (useofshower andbath, useofdryerandwashingmachine)basedonspeciﬁcactivities likecooking,cleaning,orhobbies.Inaddition,thestand-byand on/offfunctionsandbatterychargedappliancesmustbestudied moreindetail.

Understandingtheoccupantbehaviorwillbeevenmore impor-tantinfutureforefﬁciencyofelectricityuse. Findingsfromthis research could help improving design of objects, systems and architecturaldesign inorder toreduce energy consumption by occupantsathome.

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