Characterising Domestic Electricity Consumption Patterns by Dwelling and Occupant Socio PDF
Preview Characterising Domestic Electricity Consumption Patterns by Dwelling and Occupant Socio
TTeecchhnnoollooggiiccaall UUnniivveerrssiittyy DDuubblliinn AARRRROOWW@@TTUU DDuubblliinn Articles Dublin Energy Lab 2012-2 CChhaarraacctteerriissiinngg DDoommeessttiicc EElleeccttrriicciittyy CCoonnssuummppttiioonn PPaatttteerrnnss bbyy DDwweelllliinngg aanndd OOccccuuppaanntt SSoocciioo--eeccoonnoommiicc VVaarriiaabblleess:: aann IIrriisshh CCaassee SSttuuddyy Fintan McLoughlin Technological University Dublin Aidan Duffy Technological University Dublin, [email protected] Michael Conlon Technological University Dublin, [email protected] Follow this and additional works at: https://arrow.tudublin.ie/dubenart Part of the Electrical and Electronics Commons RReeccoommmmeennddeedd CCiittaattiioonn McLoughlin, F., Duffy, A. & Conlon, M. (2012). Characterising domestic electricity consumption patterns by dwelling and occupant socio-economic variables: an Irish case study. Energy and Buildings, vol. 48, May, pp.240-248. doi:10.1016/j.enbuild.2012.01.037 This Article is brought to you for free and open access by the Dublin Energy Lab at ARROW@TU Dublin. It has been accepted for inclusion in Articles by an authorized administrator of ARROW@TU Dublin. For more information, please contact [email protected], [email protected]. 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Locationin Query/Remark:clickontheQlinktogo article Pleaseinsertyourreplyorcorrectionatthecorrespondinglineintheproof Q1 Pleaseconfirmthatgivennamesandsurnameshavebeenidentifiedcorrectly. Thankyouforyourassistance. G Model ARTICLE IN PRESS EnergyandBuildingsxx(2012)xxx–xxx ContentslistsavailableatSciVerseScienceDirect Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild Highlights EnergyandBuildingsxx (2012)xxx–xxx Characterisingdomesticelectricityconsumptionpatternsbydwellingandoccupant socio-economicvariables:AnIrishcasestudy FintanMcLoughlin∗,AidanDuffy,MichaelConlon (cid:2)Weexaminetheinfluenceofdwellingandoccupantcharacteristicsondomesticelectricityconsumption.(cid:2)Amultiplelinearregression m ode lwasapp lied tofourel ect ricalpara met ers.(cid:2)Ele ctricityconsum pti onisstron glyinfluen cedbynumb er of bedroom sand household compo sitio n.(cid:2)Tim eo fuse ofelectri citydemand is stronglyin fluencedbyoc cu pantcha racteristics . ENB35881 G Model ARTICLE IN PRESS ENB35881–9 EnergyandBuildingsxxx(2012)xxx–xxx ContentslistsavailableatSciVerseScienceDirect Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild Characterising domestic electricity consumption patterns by dwelling and 1 occupant socio-economic variables: An Irish case study 2 3 Q1 FintanMcLoughlina,∗,AidanDuffya,MichaelConlonb 4 aSchoolofCivilandBuildingServicesandDublinEnergyLab,DublinInstituteofTechnology,BoltonSt.,Dublin1,Ireland 5 bSchool of Elect rical Enginee ringSyst ems andDu blinEne rgy Lab,Du blinInsti tut eofTechnol ogy,Ke vin St.,Dub lin 4,Ireland 6 a r t i c l e i n f o a b s t r a c t 7 8 9 Articlehistory: Thispaperexaminestheinfluenceofdwellingandoccupantcharacteristicsondomesticelectricitycon- 10 Receiv ed22August2011 sum ptionp atternsb yan alysingda ta obtained from asmart meteringsurv ey ofarepre sentativec ross 1112 RAeccceepivteedd i3n0 r Jeavnisueadry fo2r0m1 219 January 2012 section of approxim ate ly 4200 d omes tic Irish d wellin g s. A mu ltiple line ar regre ss io n model was ap plied tofourparameters:totalelectricityconsumption,maximumdemand,loadfactorandtimeofuse(ToU)of 13 ma xim umelectricit ydem andfora numberofdiff erentdwel lingando ccup antso cio-e cono m icv ariable s. 14 Keywords: Inparticula r,dwelling type,nu mb e rofbedr oo ms,head ofhouse hold (HoH)ag e,householdcom position, 15 Domesticelectricityconsumption socialclass,waterheatingandcookingtypeallhadasignificantinfluenceovertotaldomesticelectricity 16 Dwellingandoccupantcharacteristics consumption.Maximumelectricitydemandwassignificantlyinfluencedbyhouseholdcompositionas 17 Electricityloadprofiles wellaswaterheatingandcookingtype.Astrongrelationshipalsoexistedbetweenmaximumdemand andmosthouseholdappliancesbut,inparticular,tumbledryers,dishwashersandelectriccookershadthe greatestinfluenceoverthisparameter.Timeofuse(ToU)formaximumelectricitydemandwasfoundto bestronglyinfluencedbyoccupantcharacteristics,HoHageandhouseholdcomposition.Youngerheadof householdsweremoreinclinedtouseelectricitylaterintheeveningthanolderoccupants.Theappliance thatshowedthegreatestpotentialforshiftingdemandawayfrompeaktimeusewasthedishwasher. ©2012PublishedbyElsevierB.V. 18 1. Introduction whilstthenon-tradingsectorlargelyconsistsoftransportandagri- 37 culture along with heat use in buildings. The Irish Government 38 19 Throughout the EU, there has been a move towards smarter hascommittedtoachievinga20%reduction(comparedtoaverage 39 20 electricitynetworks,whereincreasedcontroloverelectricitygen- energyuseovertheperiod2001–2005)inenergydemandacross 40 21 erationandconsumptionhasbeenachievedwithimprovementsin thewholeoftheeconomythroughenergyefficiencymeasuresby 41 22 newtechnologiessuchasAdvancedMeteringInfrastructure(AMI). 2020[2]andhasalsosetatargetof40%electricityconsumption 42 23 Residentialsmartmeteringispartofthisandisseenasanecessary fromrenewablesourcesby2020[3].OtherEUcountrieshavecom- 43 24 pre-requisite for the realisation of EU policy goals for increased mittedtoachievingsimilartargetstothatoutlinedabove. 44 25 renewableenergypenetration,residentialdemandsidemanage- Electricity consumption patterns for domestic dwellings are 45 26 ment opportunities and improvements in energy efficiency, for highlystochastic,oftenchangingconsiderablybetweencustomers. 46 27 achievingambitious20/20/20targets. Fig.1showstwoindividualcustomerelectricityloadprofiles,over 47 28 EU-27energy-relatedgreenhousegasemissions(GHG)targets a24hperiodforarandomday.Thedifferencesbetweenthecus- 48 29 for2020(basedona2005emissionsbaseline)includeareduction tomersareapparentwithCustomer1havingtwodistinctpeaks, 49 30 of21%ingreenhousegasemissionsfortheemissiontradingsector oneinthelatemorningandanotherintheeveningtime.Customer 50 31 acrosstheEU-27countriesanda10%reductionforthenon-trading 2’sprofileontheotherhandhasadoublepeakinthelatemorning 51 32 sectoracrosstheEU.The10%reductionacrosstheEU-27countries andnosignificantpeaksintheafternoonoreveningperiods. 52 33 forthenon-tradingsectorisbrokenupcollectivelyforthedifferent Residential smart meters have been installed in a number of 53 34 memberstates.Irelandhasbeenassignedatargetof20%reduc- countries around the world such as: Italy, Sweden, Netherlands, 54 35 tioningreenhousegasemissionsby2020[1].Domesticelectricity CanadaandNorthernIreland[4].InJuly2009,thelargestelectric- 55 36 consumptioniscoveredundertheemissionstradingsectorscheme itysupplierintheRepublicofIreland–ElectricIreland(formally 56 ElectricitySupplyBoard)–commencedasmartmeteringtrialfor 57 the domestic sector and small-to-medium enterprises. The trial 58 consisted of metering approximately 4200 residential electricity 59 ∗ Correspondingauthor.Tel.:+35314023918;fax:+35314024035. customersathalfhourlyintervalsaswellasrecordingadetailed 60 E-mailaddress: fintan.m clou ghlin @dit.ie(F.M cLo ughli n). listofsoci o-e cono mic,de mograph ic andd w ellingchar ac teristics 61 0378-7788/$–seefrontmatter©2012PublishedbyElsevierB.V. doi:10.1016/j.enbuild.2012.01.037 Please cite this article in press as: F. McLoughlin, et al., Characterising domestic electricity consumption patterns by dwelling and occupantsocio-economicvariables:AnIrishcasestudy,EnergyBuildings(2012),doi:10.1016/j.enbuild.2012.01.037 G Model ARTICLE IN PRESS ENB35881–9 2 F.McLoughlinetal./EnergyandBuildingsxxx(2012)xxx–xxx Fig.1. Dailyelectricityloadprofileforanindividualdwellingacrossa24hperiod. 62 foreachhousehold.Thecollectionofsuchadetailedlistofdwelling for the most part are considered to be a “bottom-up” modelling 95 63 and occupant characteristics, combined with half hourly meter- approachastheyusedatagatheredatthedwellingleveltoinfer 96 64 ingfor4200individualcustomersoffersauniqueopportunityto relationshipsbetweenelectricityuseanddwellingandoccupant 97 65 investigatethedriversofelectricityconsumptionpatternsinthe characteristics. 98 66 home.Thedatasetallowsadetailedanalysisofnotonlytheaffectof Statistical/regression models are particularly useful when a 99 67 dwellingandoccupantcharacteristicsontotalelectricitydemand large dataset exists as they are based on real data and give a 100 68 butalsoonotherloadprofilepropertiessuchasmaximumdemand, good understanding of electricity consumption patterns. How- 101 69 loadfactorandtimeofuse(ToU)ofmaximumelectricitydemand. ever,theycanbecostlytoimplementandsometimessufferfrom 102 70 Theaimofthispaperistopresentresultsfordwellingandoccu- multi-collinearity between variables. O’Doherty et al. [5] used 103 71 pant characteristics that most significantly influence electricity data from a National Survey of Housing Quality and applied a 104 72 consumptionpatternsinthehome.Asaresultcertaingroupsmay Papke-Wooldridge generalised linear model to infer a relation- 105 73 betargetedwhereelectricitysavingsandhighrenewableenergy ship between appliance ownership and electricity consumption. 106 74 penetrationcanbeachieved,therebycontributingtowardsmeet- Theiranalysisshowedexplanatoryvariablesthathadahighsignifi- 107 75 ingEUpolicygoals.Similarly,bydeterminingelectricalappliance canceforelectricityconsumptionsuchas:dwellingcharacteristics; 108 76 characteristicsthatinfluenceelectricityconsumptionpatternsat location, value and dwelling type as well as occupant character- 109 77 peaktimeswillenablepolicymakerstoidentifymeasurestohelp istics; income, age, period of residency, social class and tenure 110 78 reducemaximumdemand. type.LeahyandLyons[6]appliedanordinarylinearleastsquares 111 regression using Irish Household Budget Survey data. Dispos- 112 able income, household size, dwelling age and socio-economic 113 79 2. Literature groupwereamongstthevariablesthatwereshowntoinfluence 114 electricity consumption in the home. A variant of the statisti- 115 80 Therearevariousdifferentapproachestomodellingdomestic cal/regressionapproachisaConditionalDemandModel(CDA)first 116 81 electricityconsumption,eachwiththeirindividualstrengthsand developed by Parti and Parti [7]. Monthly electricity bills over a 117 82 weaknesses.Theliteraturehasbeencategorisedbelowintermsof yearlyperiodwereregressedagainstapplianceownershipfigures 118 83 techniqueapplied: anddemographicvariablessuchashouseholdincomeandnumber 119 ofoccupantstodisaggregateelectricitydemandinto16different 120 84 • Statistical/regression en d-uses.Thi sm ethodologys howedthe highsign ifica nce ofappli- 121 85 • Engineering anceowne rshi poverelectric ityconsu mp tion patternsacro ss a24h 122 86 • Neural network perio d. 123 Yohanisetal.[8]analysedpatternsofelectricityconsumption 124 87 Statistical/regression models can be considered to be both a in27representativedwellingsinNorthernIreland.Electricityload 125 88 “top-down” and a “bottom-up” method of modelling. Top-down profileswerecharacterisedbasedondwellingtype,floorarea,num- 126 89 approaches take data collected at an aggregate level such as berofoccupants,numberofbedrooms,tenure,occupantageand 127 90 national energy statistics, GDP and population figures to derive household income. In particular, the authors found a significant 128 91 causal relationships between determinants and electricity con- relationshipbetweendomesticelectricityconsumptionandfloor 129 92 sumption. Bottom-up models use data collected at an individual area.HartanddeDear[9]usedregressiontodeterminearelation- 130 93 dwellingleveltodeterminerelationshipsbetweenhouseholdchar- shipbetweenexternaltemperatureandhouseholdelectricitycon- 131 94 acteristics and electricity use. Engineering and neural networks sumptioninNewSouthWales,Australia.Theirresearchconcluded 132 Please cite this article in press as: F. McLoughlin, et al., Characterising domestic electricity consumption patterns by dwelling and occupantsocio-economicvariables:AnIrishcasestudy,EnergyBuildings(2012),doi:10.1016/j.enbuild.2012.01.037 G Model ARTICLE IN PRESS ENB35881–9 F.McLoughlinetal./EnergyandBuildingsxxx(2012)xxx–xxx 3 133 that there was a significant relationship between external tem- consumptioninthehomeaswellastheinfluenceofeachparam- 199 134 perature and electricity consumption and that this tended to be eteroneachother.Theirselflearningcapabilitiescanresultinan 200 135 strongerduringperiodsofcoolerweather.Parker[10]alsolooked accurate means of modelling electricity consumption within the 201 136 attheeffectofexternaltemperatureonelectricityconsumptionby home. However, like CDA, neural networks can also suffer from 202 137 applyinglinearregression.Fifteenminutedatawascollectedfrom multi-collinearityissueswherehighlevelsofappliancesaturation 203 138 204residencesinCentralFlorida,USA,lookingattotalelectricity exist. Aydinalp et al. [16] developed a neural network to model 204 139 consumption,spaceheating/coolingandwaterheating.Asignifi- electricity consumption for domestic appliances, lighting and 205 140 cantrelationshipwasalsofoundbetweenallelectricityend-uses spacecoolinginthehome.Aydinalpetal.[17]extendedthiswork 206 141 and external temperature. However, it is important to note that to develop neural network models for space and domestic hot- 207 142 bothprecedingstudiespresentedbyHartanddeDearandParker waterheating.Aydinalpetal.[18]alsocarriedoutacomparison 208 143 were carried out in hot climates where electricity is commonly ofneuralnetwork,conditionaldemandanalysisandengineering 209 144 used to heat and cool homes,somethingwhichis not replicated approaches to modelling end-use energy consumption in the 210 145 in more temperate climates such as the United Kingdom and residentialsector.Variablesusedintheneuralnetworkmodelthat 211 146 Ireland. influencedelectricityconsumptionwereapplianceownershipand 212 147 Engineeringmodelsuseinformationsuchasappliancepower usage,income,dwellingtypeandhouseholdcomposition. 213 148 ratingsorend-usecharacteristicstobuildupadescriptionofelec- Pastliteraturehasidentifiedkeyvariablesthatinfluenceelec- 214 149 tricity consumption patterns from the “bottom-up”. One of the tricityconsumptioninthehome[5–13,16,19–27].Fig.2ranksthe 215 150 majorstrengthsassociatedwithsuchmodelsisthattheyarethe number of citations of each of these variables in this literature. 216 151 onlymethodologythatcanmodelelectricityconsumptionwithout Thetopfourvariables,dwellingtype,householdincome,appliance 217 152 anyhistoricalinformationonelectricityuse.However,engineer- holdingsandnumberofoccupantsappearfrequentlyinthelitera- 218 153 ingmodelscanbecomplextoimplementandneedtobevalidated. ture.However,itisimportanttonotethatthefrequentoccurrence 219 154 Yao and Steemers [11] developed a dynamic software model to of certain variables may also be a consequence of the ease with 220 155 generate load profiles based on occupancy patterns, appliance whichdatawascollected.Forinstance,datarelatingtothetopfour 221 156 ownership and ratings. The authors categorised electricity con- variablescitedinFig.2canbeobtainedfromnationalcensusand 222 157 sumptiondeterminantsbasedontwocategories:behaviouraland householdbudgetsurveyswithrelativeease.Othervariablessuch 223 158 physical, both of which are strongly related to dwelling occu- asfloorareamaybeoverlookedduetothedifficultywithwhich 224 159 pancypatterns.Behaviouraldeterminantsrelatetodecisionsmade thisinformationisgathered,particularlyforlargesamplesizes. 225 160 on a hourly/daily/weekly basis regarding use of particular appli- Dwelling and household characteristics used in the analysis 226 161 ances.Physicaldeterminantsrelateto“fixed”variablesthatdonot werebasedontherankingsystemshowninFig.2andtheinforma- 227 162 changeoftenoratallwithtimesuchasdwellingsize.Widenand tionthatwasavailablefromthesmartmeteringsurvey.Yohanis 228 163 Wackelgard [12] used time-use data (i.e. occupant’s schedule of et al. [8] showed that electricity consumption was highly corre- 229 164 living activities) as well as appliance holdings, ratings and day- latedtonumberofbedrooms.Forthisreasonandbecausereliable 230 165 light distributions to produce electricity load profiles. Three sets dataonfloorareawasnotavailablefromthesmartmeteringsur- 231 166 ofSwedishtime-usedataandenergymeasurementswereusedto vey,numberofbedroomswasusedasaproxyinstead.Santamouris 232 167 model and validate results. The authors found it to be an effec- etal.[28]foundasignificantrelationshipbetweenincomegroups 233 168 tivewayofgeneratingindividualloadprofiles.Shimodaetal.[13] anddomesticenergyconsumption.Theinformationgatheredon 234 169 modelled electricity consumption on an hourly basis for differ- householdincomefromthesmartmeteringsurveywasfoundtobe 235 170 ent dwelling and household characteristics in Osaka city, Japan. unreliableandthereforeanothermeansofdeterminingthiseffect 236 171 The authors showed that occupant’s time-use, external temper- was sought. The Irish National Employment Survey 2008–2009 237 172 ature,applianceefficienciesanddwellingthermalcharacteristics [29]showedarelationshipbetweenincomeandsocialclassand 238 173 all significantly influenced the electricity consumption pattern thereforethisvariablewasusedasaproxyinstead.Thelocation 239 174 acrosstheday.Capassoetal.[14]modelledelectricityconsump- of individual dwellings was not included in the analysis as the 240 175 tion patterns at a 15min period, where various socioeconomic, survey did not record this information. Dwelling age and tenure 241 176 demographic, psychological and behavioural characteristics of a typewerefoundtobehighlycorrelatedwithHoHageandcaused 242 177 homeowneraswellasappliancecharacteristicswereusedtopro- multi-collinearitybetweenvariablesandthereforeonlyHoHage 243 178 duceanelectricityloadprofile.Homeowner’soccupancypatterns wasincludedforthatreason.Similarlynumberofoccupantswas 244 179 as well as appliance ownership, usage and ratings contributed highlycorrelatedwithhouseholdcomposition.Externaltempera- 245 180 significantly to constructing the load profile shapes. Papadopou- turewasnotincludedasairconditioningispracticallynon-existent 246 181 los et al. [15] applied EnergyPlus simulation software to model inthedomesticsectorinIrelandandelectricspaceheatingonly 247 182 two multifamily domestic buildings energy use to determine constitutedaverysmallproportionofthesample(lessthan3%). 248 183 theoptimumeconomicandenvironmentalperformanceofspace Anefficiencyvariablewasincludedtodetermineindividualcus- 249 184 heating types in two Greek cities. The authors compared three tomer’sintentionstoreducetheiroverallelectricityconsumption 250 185 types:oilfiredboiler,heatpumpsandelectricradiatorsandgas whichwillbediscussedlater. 251 186 fired boilers, with the latter outperforming the other two types 187 significantly.However,theauthorsalsoconcludedthatundercer- 188 tain circumstances electrically driven heat pumps can rival gas 3. Methodology 252 189 fired space heating and favour renewable energy production in 190 thehome. Thedatasetusedintheanalysiswastakenfromapopulation 253 191 Neuralnetworksuseamathematicalmodelofbiologicalnet- of345,645households.Thepopulationwasdividedintosixgroups 254 192 works to simulate electricity consumption in a dwelling. It is a basedontotalannualhouseholdelectricityconsumptiontoensure 255 193 variantoftheengineeringsubgroup,modellinginputdeterminant anevenspreadofelectricityconsumingcustomers.Aninitialsam- 256 194 variables as a series of neurons. Each neuron can interact with pleof5574wasdrawnonarandomisedbasisacrossallprofiles. 257 195 othersthroughafeedbackmechanism.Historicallytheyhavebeen This was subsequently reduced to 5375 households by targeting 258 196 usedtoforecastelectricitydemandatautilitylevel,however,they certain groups to improve representivity of dwelling and socio- 259 197 havealsobeenappliedatadomesticlevel.Neuralnetworksmodel economic variables within the sample size. A final sample size 260 198 a complex number of input parameters that affect electricity of3941householdswasusedintheanalysis,oncelargeoutliers 261 Please cite this article in press as: F. McLoughlin, et al., Characterising domestic electricity consumption patterns by dwelling and occupantsocio-economicvariables:AnIrishcasestudy,EnergyBuildings(2012),doi:10.1016/j.enbuild.2012.01.037 G Model ARTICLE IN PRESS ENB35881–9 4 F.McLoughlinetal./EnergyandBuildingsxxx(2012)xxx–xxx Fig.2. Dwellingandoccupantcharacteristicsthatinfluencedomesticelectricityconsumptionpatterns. 262 andnon-continuousdata(aresultoftechnologycommunication numberofperiodsinadayandmisthetotalnumberofdaysover 293 263 errors)wereremoved.Dwellingandoccupantcharacteristicswere thesixmonthperiod. 294 264 collectedbymeansofaphoneinterview. (cid:2)m 226656 DecIenmitibael lry 2a 0 0si9x wm aosn tuh s epderaiso da bbeetnwcehemn a1rkst tJouleyn 2su00re9 aalnlds m31asrtt EMD= m1 max{Ei, 1 ≤ i ≤ n} (2) 295 267 meters, co mmun icati on an d I T systems we re function ing satis- j=1 268 factorily. After this period, the customers were subjected to four Daily load factor, ELF is a ratio and is shown in Eq. (3). It is a 296 269 differenttariffstructuresandfourdifferentstimulitoinvestigate measureofdailymeantodailymaximumelectricaldemandandis 297 270 theimpactondrivingdemandreductionoverthecalendaryearfor ameasureofthe“peakyness”ofacustomer’sloadprofile.Typically, 298 271 2010.Acontrolgroupof1000customerswasunaffectedbythese largerloadfactorscorrespondtocustomerswhoconsumeelectric- 299 272 measuresovertheyearlyperiod.Asthispaperwasprimarilycon- itymoreevenlyacrossthedaywhereasalowloadfactorindicates 300 273 cerned with investigating dwelling and occupant characteristics smallintervalsoflargeelectricityconsumption.Eq.(3)describes 301 274 that are most influential in affecting domestic electricity demand, daily load factor, ELF, where Eiis electrical demand in kW over each 302 275 thebenchmarkperiodofsixmonthswasusedfortheanalysisdue halfhourperiod,nisthetotalnumberofperiodsinadayandmis 303 276 toitslargesamplesizeandindependencefromanytariffchanges thetotalnumberofdaysoverthesixmonthperiod. 304 222777789 oterr siTsthit miicssu plaain.p derh eoxuasme hinoleds athpep leifafneccet sofo dnwfoeullrin dge apnedn doecnc utppaanra t mcheatrearcs-: ELF = m1(cid:2) m ma(x1{/ Eni,) (cid:3)1≤ni=i1 E≤in} (3) 305 280 totalele ctric ityconsum ption,max im umd emand,loa dfactorand j=1 222288881234 tppeilaamertctaetem rrioncefast eliurnpss a etwrh aee(mTr heoe oUctmeh)r oesos efoa nrvme esroap x arae ismss ietxuon m mtdee odsenclitrenhicb tEaerqni ecsdil.te y2(c1 4t)r ch–ioc (nip4tse)y.ur cimEooTdnpO.sTt iuAToLhmnei.ps ftTotiohhuneer dtwreihcfiAiinct yhem dicta oboxnycism cEuuqumr.ms(p(4 tw)TioowhnUeh r ieenpr a1ear = Eadmi0ar0yee: pt3aer0nre,das nEe jTndmota4Usx8 toch=voe0er m0rr e:aa0s xp0sio)im,xnn dumisms ott hnovea tthlthu oepet eaotlrifmin oeuedlem acis--t 333300006789 285 totalamo untofelect ricit yconsume do vera sixmo nthperio din berof pe riodsi naday a nd mis the tot al numbe r of day sove rthe 310 286 kWh whereE iis electricald emandink Wfo re ach halfho urperi od sixm o nthperi od .T oUi ndic ate s thet imeo fdayatw h ichm axim um 311 287 and li stheto taln umberof half-hou rly per iod sove rthe sixm onths. ele ctricity consum ptio noccurs. 312 (cid:2)l EToU= mode{jmax|Ejmax = max{Ei, 1 + n(j − 1) ≤ i ≤ n, 1 ≤ j ≤ m}} 313 1 288 ETOTAL= 2 Ei (1) (4) 314 i=1 315 Multiplelinearregressionwasappliedtomodelthevariation 316 289 Eq.(2)describesmeandailymaximumdemand,EMDoverasix in electrical parameters presented above due its suitability in 317 290 monthperiodinkW.EMD referstothelargestvalueofelectrical handling large amounts of qualitative data corresponding to 318 291 demand in a day, averaged over a six month period where Ei is occupant socio-economic variables, and also its extensive use in 319 292 electricaldemandinkWforeachhalfhourperiod,nisthetotal literaturetomodelelectricitydemandprofiles[5–7,19–22].Two 320 Please cite this article in press as: F. McLoughlin, et al., Characterising domestic electricity consumption patterns by dwelling and occupantsocio-economicvariables:AnIrishcasestudy,EnergyBuildings(2012),doi:10.1016/j.enbuild.2012.01.037 G Model ARTICLE IN PRESS ENB35881–9 F.McLoughlinetal./EnergyandBuildingsxxx(2012)xxx–xxx 5 Table1 Descriptivestatisticsforelectricalparameters. Parameter Mean Median Standard Maximum Minimum Probability Probability deviation distributionscale distributionshape parameter((cid:2)) parameter(ˇ) Total electricity consumption (ETOTAL) 2261 kW h 2142 kW h 1108 kW h 10,065 kW h 99 kW h 2555 2.15 MLoaaxdi mfauctmord (eEmLF)a nd (EMD) 22.35.04 3k%W 22.24.95 3k%W 16..0313 %k W 78.23.600 k%W 08..0173 %k W 2−.18.14 873a 20..163589a ToU (EToU) 31.40 35.00 9.85 n/a n/a n/a n/a Weibullprobabilitydistributionfunctionf(T)=ˇ/(cid:2)(T/(cid:2))ˇ−1e−(T/(cid:2))ˇ where f(T)≥0, T≥0, ˇ>0, (cid:2)>0. a Log –logisticprobabilitydis tribution f unc tio nf(T)=ez/(ˇT(1+ez)z) w he re z = (T(cid:5) − (cid:2))/ ˇ, T (cid:5)= ln( T) , 0<T<∞, −∞<(cid:2)<∞, 0<ˇ<∞. 321 different models were developed: first looking at dwelling and 322 occupantvariablesandsecondlylookingatindividualappliances 323 thatinfluencedelectricityconsumptionpatternsinthehome.The Table2 324 first model, dwe llingand o ccupant chara cteristics (D OC) , descr ibes List of independent variables used in regression model. 325 thevariablesthatinfluenceelectricityconsumptioninthehome Variablename Variableexplanation Sample 326 suc h as HoH age and numb er of occu pants and be dr oom s, etc. size(N) 332278 Tohcceuspe avnatrsiadbelems adnod nowt i“tchoinnstuhmeeh”o emleectraincditym bauyt sheervlpe teox pinlafliunenthcee Dwell type detach Dbuwneglalilnogw iss) detached (includes 2068 329 underlining causes of different patterns of electricity use. The Dwelltypesemid Dwellingissemi-detached 1230 330 secondmod el,elect rica lapplianc es(EA),lo ok sdirectlyat the indi- Dwell type terr Dwelling is terraced 569 Dwelltypeapt Dwellingisapartment 67 331 vidual appliances and describes the direct relationship between 332 theirownershipanduseonelectricityconsumptionpatternswithin Nobedrooms Dwellinghasonetosix 3941 333 the h ousehold. This mo de l serves to give a bette r predic tion of 1 –6 bedroom s 334 patternsofelectricityusebutdoesnotexplainunderliningcauses. HoHage1835 Headofhouseholdage 390 between18and35 335 4. Resultsanddiscussion HoHage3655 Headofh ou seholdage 1776 between36and55 336 Descriptivestatisticssuchasmean,medianandstandarddevi- HoH age 56plus Head of household age above 1753 55 337 ationvaluesarepresentedforeachelectricalparameterinTable1. 338 Probability distribution functions are fitted to Eqs. (1)–(3), with HHcomplivealone Householdcomposition–live 756 339 scaleandshapeparametersalsopresentedinthetable. alone 340 A mult iple lin ear regressi on w as carried ou t us ing the following HH comp with adults wHoituhseahdoulldts composition – live 2064 341 variables:dwellingtype,numberofbedrooms,headofhousehold HHcompwithadultsandHchoiuldserehnoldcomposition–live 1121 342 (HoH)age,householdcomposition,HoHsocialclass,waterheating withadults andchildren 343 type,c ooki ngtypean danefficienc yind icator .Aful llistin gofthe 344 independentvariablesusedintheanalysisareshowninTable2, HoHsocialclassAB Highandintermediate 593 345 withbaseva riablehigh light ed inb olditalic sw heredu mm ycat e- mana geri al, administrative or professional 334467 gorOictahl evrariniadbelepse nardee nutsevda.riables tested for significance included HH social class C jSuunpieorrvmisoanrya gaenrdia cll,esrkiiclalel dand 1697 348 dwellingage,HoHemploymentstatus,tenuretype,HoHeducation manualworkers 349 level and spa ce he ating type. T hese va riables were om itted from HHsocialclassDE mSeamniu-askl iwlloedrk aenrsd, ustnastkeilled 1505 350 theanalysissincetheyeithershowedlittleornosignificanceover pensioners,unemployed 351 thetestedparametersorshowedahighdegreeofmulti-collinearity HHsocialclassF Farmers 107 352 wit hothe rindepende nt variable s .Inp articula r, HoHageshowed 353 strongcollinearitywithdwellingageandtenuretypewithPearson Waterheatnonelectric Waterisheatedbyother(oil, 3144 354 correla tion coeffic ients exceedin g 35 % in both cases . This can be gas, so lid fuel) Waterheatelectric Waterisheatedbyelectricity 771 355 explainedbyyoungerHoH’shavingahigherpercentageofmort- 356 gages and occupying newer dwellings. In comparison, a higher CookingtypenonelectricCookingismostlydoneby 1192 357 percentageofolderHoH’shavetheirmortgagepaidoffandlivein non-electricmeans(oil,gas, 358 older dwell ing s. Ho H emp loym ent st atus and e duca tion lev el h ad Cookingtypeelectric sCooloidk ifnugeli)sm ostlyd oneb y 2749 359 littleeffectontheparametersandshowedhighcollinearitytoHoH electricity 360 social class with Pearson correlation coefficients exceeding 25%. 361 Spaceheatingtype(electricandnon-electric)hadnosignificance Efficiencyless10 HoHwhobelievetheycancut 1950 362 atalloverthefourparameters,duetotheverylowpenetrationof electricityconsumptionby 363 ele ctr iche atin g(le ssthan3%)in Ire lan d. 10% Efficiencybetw1020 HoHwhobelievetheycancut 916 364 Table3showstheresultsforthelinearregressionfortheDOC electricityconsumptionby 365 modelandeachofthefourdependentparameterswithvariables between1 0%&20% 366 listedi nTa ble2. Th esig nific anceofvari ablesoneac hpar ameteris Efficiencybetw2030 HoHwho beli ev etheycancut 345 367 shown b y way of a p v alue, indicat in g 90%, 95% an d 99% significanc e ebleetcwt reiecnity2 0co%n&su3m0 %ptio n by 368 levels. Efficiencymore30 HoHwhobelievetheycancut 123 336790 moLdienlewarit hretghreesssaimone fwouars dceaprerinedde notupt aar asmeceotenrds atismbee foforre athned fiEfA- emleocrte ritchiatyn c3o0n%sum ptio n by 371 teencommonhouseholdappliancesasexplanatoryvariables.The 372 results are presented in Table 4 alongside household appliance Please cite this article in press as: F. McLoughlin, et al., Characterising domestic electricity consumption patterns by dwelling and occupantsocio-economicvariables:AnIrishcasestudy,EnergyBuildings(2012),doi:10.1016/j.enbuild.2012.01.037 G Model ARTICLE IN PRESS ENB35881–9 6 F.McLoughlinetal./EnergyandBuildingsxxx(2012)xxx–xxx Table3 Regressionresultsfordwellingandoccupantcharacteristicsmodel(DOC). Totalelecconsumption Maximumdemand Loadfactor ToU Coef. Std.error Coef. Std.error Coef. Std.error Coef. Std.error (Constant) 18.6055 101.633 0.6388*** 0.092 0.2169*** 0.0068 29.4659*** 1.0786 Dwelltype semid −175.6725 *** 34.1701 −0.0766** 0.0309 −0.0082*** 0.0023 −0.414 0.3626 Dwell type terr −147.045*** 45.9229 −0.0583 0.0416 −0.0114*** 0.0031 −1.287 2** 0.4874 Dwell type apt −245.5571** 119.4231 −0.2963 ** 0.1081 0.0084 0.008 0.1958 1.2674 Nobedrooms 349.036*** 19.9182 0.2365*** 0.018 0.0089 *** 0.001 3 0.6785 *** 0.2114 HoHage365 5 282.8721*** 51.7462 0.0722 0.046 8 0.0171*** 0.0034 −0.9431* 0.5492 HoH age 56 plu s 212.0358*** 57.7676 −0.1515 *** 0.0523 0.0318*** 0.0038 −2.0417*** 0.6131 HHc omp w ithad ults 730.9512*** 40.7046 0.7036*** 0.0368 −0.0022 0.0027 1.2854*** 0.432 HH comp with adults 1083.688*** 50.2313 0.9853*** 0.0455 0.0043 0.0033 2.0295*** 0.5331 andchildren HoHsocialclassC −73.6939* 44.1127 0.0407 0.0399 −0.0134*** 0.0029 1.2344** 0.4682 HoHsocialclassDE −132.952** 48.522 −0.0146 0.0439 −0.0155*** 0.0032 0.8489 0.515 HoHsocial class F −370.2021*** 98.0024 −0.2591 *** 0.0887 −0.0016 0.0065 −2.8708* * 1.0401 Wate rheat elect ri c 148.9229*** 29.5042 0.2379*** 0.0267 −0.0077 *** 0.002 −1.3368*** 0.3131 Cookin gtyp eelectr ic 185.6567*** 32.2118 0.3896*** 0.0292 −0.0241*** 0.002 1 0.1381 0.3419 Efficienc ybet w1020 142.7689*** 37.6209 0.1139*** 0.0341 0.0015 0.0025 −0.4104 0.3993 Efficiency betw 20 30 188.2471*** 54.1685 0.1638*** 0.049 0.0021 0.0036 −0.2999 0.5749 Efficiency more 30 274.1978*** 85.5507 0.1476* 0.077 4 0.0089 0.0057 −0.57 0.908 Basevariables:Dwellingtypedetach,HoHage1835,HHcomplivealone,HoHsocialclassAB,Waterheatnonelectric,Cookingtypenonelectric,Efficiencyless10. * p<0.1. ** p < 0.05. *** p<0.01 373 penetrationlevels.Thebasevariablechosenfortheanalysiswas olderloneHoH’s(whosechildrenhavevacatedthefamilyhome) 390 374 washingmachineduetoitshighpenetrationlevelof98.3%within to downsize, would reduce overall electricity demand for the 391 375 thesurvey. sector. 392 Electricity consumption for younger HoH’s was significantly 393 376 4.1. Totalelectricityconsumption lowerwhencomparedtotheothertwoagecategories,36–55and 394 56plu s.This couldbeat tri but edtom idd leag edHoH’sh avingmore 395 377 Total electricity consumption was regressed against dwelling childrenlivingathome(thushavingahighernumberofoccupants) 396 378 and occu pant varia bles described in T able 2 an d a coef ficient of andincre asedo cc upanc ypat terns(i. e .dwelli ngoccup an tsathome 397 379 dete rmination of32%wa srecorded fo rtheD OC mo de l.Alldwelli ng for l onger per iods of the day). Th is i s also app arent whe n look- 398 380 typeshadane ga tive effec tontotal ele ctri city consum pti onwhen ing athous eholdco mp osit ion:a dults liv ingw ithchildr encon sume 399 381 comp ared to thebase varia ble detac heddwell ing,whichinc luded con sid erablymo reelectricityt hanth oseliv inga loneorw ithother 400 382 bungalows .A sex pect ed,apart mentshad significa ntlylo wertotal adults. HoH social class had a neg ative effect on to tal elect ricity 401 383 electricityc ons umption thanallothe rdw ellingtypes ,aresu ltof consum ption when com pare d againstth ebas eca tegor yAB,rep- 402 384 theirsmal lersizeandfe wern um bero foccupan ts.For ea chadd i- resentingHig herPro fessionals .Socialc lass was usedasa pro xyin 403 385 tiona lbedroo m,to tale lectric ityconsu m ptiononave rag eincr eased the absen ce of re liable data on house hold inco me. T hi s sugges ts 404 386 349kW h over t he si x month p eriod. On a p er capita b asis, total that Higher Pro fessiona lsare in clinedtoco nsumem ore electric- 405 387 elec trici ty con sum ptio n for t he resid ent ial sec tor acc ounte d for ity t han Low er Profession als with the fo rmer ten ding t o live in 406 388 948kWho verthesixm ont hpe riod.Thiss uggests thatplann ing lar gerdw ellings andhaveagre atern um berofel ectricala pp lianc es, 407 389 laws in fa vour ofsm all erdwe llingsor apro pertytax toe ncourage sugges tingapos sible inco m eeffec t. 408 Table4 Regressionresultsforelectricalappliancesmodel(EA). Householdappliance Totalelecconsumption Maximumdemand Loadfactor ToU penetration Coef. Std.error Coef. Std.error Coef. Std.error Coef. Std.error (Constant) 656.9107*** 51.3526 0.8771*** 0.0472 0.2444*** 0.0035 29.8274*** 0.5578 Tumbledry er 68% 375.3768*** 33.5586 0.3951*** 0.0309 −0.0045* 0.0023 −0.1742 0.3645 Dishwas her 67% 406.0503*** 33.7939 0.2894*** 0.0311 0.0128*** 0.0023 1.4145 *** 0.3671 Shower(instant) 69% 44.0911 32.8842 0.2557*** 0.0302 −0.0189*** 0.0022 −1.1625*** 0.3572 Shower (pumped ) 29% 34.5628 33.0484 −0.0159 0.0304 0.0025 0.0022 −0.2293 0.359 Electrica lcooker 76% 182.6508* ** 34.2263 0.3758 *** 0.0315 −0.0241 *** 0.0023 0.5208 0.3718 Heater(p luginc onvective) 30% 56.5369* 31.4838 −0.0339 0.029 0.008*** 0.0021 −1.1678 *** 0.342 Freezer (stan da lone) 50% 198.131*** 29.6764 0.0775 *** 0.027 3 0.0129*** 0.002 0.0618 0.3224 Waterp ump 20% 208.1565*** 36.7427 0.0902** 0.0338 0.0063** 0.002 5 0.7612 * 0.3991 Immer sion 77% 73.4666** 34.6355 0.1701*** 0.0319 −0.0068*** 0.0023 −0.4635 0.3762 No.TV<21 in. 66% 100.8994*** 15.8887 0.1059*** 0.0146 −0.0017 0.0011 0.434** 0.1726 No. TV > 21 in. 84% 197.2184*** 18.4409 0.1393*** 0.017 0.0026 ** 0.0012 0.5456** 0.2003 No. com put er (desktop) 48% 287.3278*** 26.4866 0.1626*** 0.024 4 0.0095*** 0.0018 0.6874** 0.2877 No. computer (laptop) 54% 135.1009*** 19.7789 0.0978*** 0.0182 0.0042*** 0.0013 0.2103 0.2149 No. gamecons oles 33% 193.1296*** 20.7689 0.1953*** 0.0191 0.0017 0.0014 0.2495 0.2256 Basevariable:washingmachine. * p<0.1. ** p < 0.05. *** p < 0.01. Please cite this article in press as: F. McLoughlin, et al., Characterising domestic electricity consumption patterns by dwelling and occupantsocio-economicvariables:AnIrishcasestudy,EnergyBuildings(2012),doi:10.1016/j.enbuild.2012.01.037 G Model ARTICLE IN PRESS ENB35881–9 F.McLoughlinetal./EnergyandBuildingsxxx(2012)xxx–xxx 7 409 Anindicatorvariablewasalsousedtomeasurepotentialhouse- and as a consequence reduce greenhouse gas emissions for the 473 410 holdelectricitysavingsbyaskingthosesurveyedtoquantifyhow sector. In particular, tumble dryers and dishwashers offer the 474 411 muchtheybelievedtheycouldcuttheirelectricityconsumptionby best opportunity for shifting demand away from peak time use 475 412 changingtheirbehaviour.Thevariableshowedstrongpositivecor- compared to electric cookers as they are less dependent on the 476 413 relationwithincreasingelectricitysavings(i.e.respondentswith timing of high priority household routines such as cooking. The 477 414 higherelectricityconsumptionbelievedtheycouldmakegreater introduction of time of use tariffs for the residential sector, so 478 415 electricitysavingsthanthosewhoconsumedless).Thissuggests that electricity consumed at peak times reflects the true cost of 479 416 thatlargerelectricityconsumersarewasteful(i.e.leavelightson generation, may encourage homeowners to shift non-essential 480 417 inunoccupiedrooms)andhencebelievetheycancutbackontheir applianceusetooffpeaktimeswhenelectricityischeaper. 481 418 electricity use. In contrast, those who consume less may believe 419 thattheyareefficientintheiruseofelectricityandcannotmake 4.3. Loadfactor 482 420 furthersubstantialcuts. 421 Table 4 shows regression results for the EA model, where a A significantly lower coefficient of determination, 9%, was 483 422 coefficientofdeterminationof32%wasrecorded.Tumbledryers, recordedforloadfactorintheDOCmodelcomparedtotheprevious 484 423 dishwashers, cookers, freezers, water pumps (used in low water two parameters. Load factor changes only slightly between cus- 485 424 pressureresidentialareas)andbrowngoods(televisions,comput- tomersasindicatedbythelowstandarddeviationfortheparameter 486 425 ers,gameconsoles)wereallsignificantatthe99%level.Showers (6%)showninTable1.However,theparameterisusefulfordeter- 487 426 showed no significance at all and immersions were only signifi- miningtheloadprofileshapeofindividualcustomers.Alowload 488 427 cantatthe90%levelresultingintheunderestimationofelectricity factorindicatescustomerswhoseelectricityconsumptionpattern 489 428 used for water heating in the home. It is also important to note ishighforshortperiodsoftimewhereasahigherloadfactorindi- 490 429 thattheanalysisaboveisindependentoflighting,whichisasig- catesamoreconstantuseofelectricityacrosstheday. 491 430 nificant contributor to electricity consumption. Lighting demand Semi-detachedandterraceddwellingshadasignificantimpact 492 431 couldnotbedistinguishedfromthesurveyasthenumberoffit- onloadfactorcomparedtothebasevariable(detacheddwelling). 493 432 tingswasnotrecorded.Similarly,electricalappliancerefrigerator Largerdwellingssuchasdetachedandsemi-detachedhomeshad 494 433 wasnotrecordedaspartofthesurvey.Asnearlyallhouseholds apositiveeffectonloadfactor.Foreachadditionalbedroom,load 495 434 willhavesomedegreeoflightingandrefrigeration,thisledtothe factoronaverageincreasedby1%.HoHagealsostronglyinfluenced 496 435 overestimationofregressioncoefficientsforotherappliancessuch loadfactorinapositivemannerwithyoungerHoHgroupshaving 497 436 astumbledryers,dishwashersandbrowngoodsinTable4. slightlylowerloadfactorsrepresentingamore“peaky”loadacross 498 theday.Incontrast,olderHoHgroupshavealargerloadfactor,indi- 499 437 4.2. Maximumdemand catingasmootherelectricityconsumptionpatternacrosstheday. 500 This is most likely due to older HoH’s living in larger dwellings, 501 438 Maximumelectricitydemandwasregressedagainstthevari- havingmorenumberofoccupantsandpossiblymoreactiveinthe 502 439 ableslistedinTable3andacoefficientofdeterminationof33%was home during the day. This was also shown by Richardson et al. 503 440 recordedfortheDOCmodel.Maximumdemandwassignificantly [30]wherehomeactivity(i.e.switchingonanelectricalappliance) 504 441 influencedbysemi-detachedandapartmentdwellingsatthe95% increaseswithnumberofoccupants.Waterheatingandcooking 505 442 levelasshowninTable3.Whencomparedagainstthebasevariable type influenced load factor in a negative manner and therefore 506 443 (detacheddwelling)eachhadanegativeinfluenceonmaximum householdsthatuseelectrictytoheatwaterandcookwilltherefore 507 444 demand, particularly apartments. Number of bedrooms was sig- tendtohavelowerloadfactors. 508 445 nificantatthe99%levelandservestoincreasemaximumdemand The EA model also recorded a coefficient of determination 509 446 by0.23kWforeveryadditionalbedroomwithinadwelling.Sim- of 9% for load factor. Most household appliances were signifi- 510 447 ilarly, household composition significantly influenced maximum cant at the 99% level except for tumble dryers, electric showers 511 448 demand,withadultsandchildrenconsumingnearlyanextrakilo- (pumped), water pumps, televisions and game consoles. When 512 449 wattcomparedtothebasevariable(adultlivingalone).Apartment comparedagainstthebasevariablewashingmachine,appliances 513 450 dwellingstendtobesmallerinsize,havefeweroccupantsandhave withnegaitivecoefficientsdecreaseloadfactorandcorespondwith 514 451 asmallerstockofappliancesthanotherdwellingtypes,allofwhich highpowerdevicesthatarenotusedcontinuouslyforlongperi- 515 452 aredriversofmaximumdemand.Asexpected,homeswithelec- odsoftime.Inparticular,electricshowers(instant),cookersand 516 453 tricwaterheatingandcookingalsohadhighermaximumdemands immersions, which are all significant at the 99% level, tended to 517 454 compared to those that use other methods to heat water and to decrease load factor due to their high power requirements and 518 455 cook. resultinamore“peaky”domesticloadprofile.Dishwashersand 519 456 TheEAmodelrecordedacoefficientofdeterminationof33%. standalonefreezersontheotherhandhadasignificantpositive 520 457 Almostallhouseholdappliancesshowedsignificantinfluenceon effectonloadfactorastheyareswitchedonforlongerperiodsof 521 458 maximumdemandatthe99%level.Pumpedshowersandplugin time. 522 459 convectiveheatersweretheonlyappliancesnottoshowanysignif- 460 icanceatall,possiblyduetotheirrespectivelowpowerratingand 4.4. Timeofuse(ToU) 523 461 offpeakuse.Thethreelargestcontributorstomaximumelectric- 462 itydemandweretumbledryers,dishwashersandelectriccookers A poor coefficient of determination of 2.6% was recorded for 524 463 whichallhavesignificantheatingcomponentsintheiroperation. ToU in the DOC model. However, the results may be somewhat 525 464 Instantelectricshowersandimmersionappliances,bothusedfor distortedduetothebi-modaldistributionoftheregressionresid- 526 465 heatingwaterwerethenextlargestcontributors. uals. Nevertheless, ToU showed high significance for household 527 466 Electricitygeneratedatpeaktimessuchasearlymorningand compositionandHoHage.ForHoHage,theoldertheheadofthe 528 467 evening times is far less efficient than electricity generated at householdthemorenegativetheinfluenceontheparameterindi- 529 468 other times of day. This is a direct result of running expensive catingearlieruseofmaximumelectricityconsumptionduringthe 530 469 peaking generation plant such as open cycle gas turbines to evening.HouseholdcompositionhadapositiveeffectonToUwith 531 470 respond to quick changes in system demand, which are less adults and children tending to use maximum electricity later in 532 471 efficient than other types of generation. Shifting demand away theeveningcomparedtooccupantslivingalone.Youngerandmid- 533 472 frompeaktimeswillresultinamoreefficientelectricitysystem dleagedgroupscorrespondtohouseholdswithyoungfamiliesand 534 Please cite this article in press as: F. McLoughlin, et al., Characterising domestic electricity consumption patterns by dwelling and occupantsocio-economicvariables:AnIrishcasestudy,EnergyBuildings(2012),doi:10.1016/j.enbuild.2012.01.037
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