A differentiated description of building-stocks for a georeferenced urban bottom-up building-stock model

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ContentslistsavailableatScienceDirect

Energy

and

Buildings

jo u r n al h om ep age :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

A

differentiated

description

of

building-stocks

for

a

georeferenced

urban

bottom-up

building-stock

model

Magnus

Österbring

a,∗

_,

_Érika

_Mata

b

_,

_Liane

_Thuvander

c

_,

_Mikael

_Mangold

a

_,

_Filip

_Johnsson

b

_,

Holger

Wallbaum

a

a_Chalmers_University_of_Technology,_Civil_and_{Environmental}_Engineering,_Sven_Hultins_gata_8,₄₁₂₉₆_Göteborg,_Sweden b_Energy_and_Environment,_Sven_Hultins_gata_8,₄₁₂₉₆_Göteborg,_Sweden

c_{Architecture,}_Sven_Hultins_gata_8,₄₁₂₉₆_Göteborg,_Sweden

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received14January2016

Receivedinrevisedform21March2016 Accepted22March2016

Availableonline23March2016 Keywords: Buildingstock GIS Energy Modelling Residential

a

b

s

t

r

a

c

t

Severalbuilding-stockmodellingtechniqueshavebeenemployedtoinvestigatetheimpactofenergy efficiencymeasures(EEM),wherethedescriptionofthebuilding-stockgenerallyconsistsofan age-typeclassificationtospecifybuildingcharacteristicsforgroupsofbuildings.Suchdescriptionslackthe appropriatelevelofdetailtodifferentiatethepotentialforEEMwithinagegroups.Thispaperproposes amethodologyforbuilding-stockdescriptionusingbuilding-specificdataandmeasuredenergyuseto augmentanage-typebuilding-stockclassification.Byintegratingbuildingcharacteristicsfromenergy performancecertificates,measuredenergyuseandenvelopeareasfroma2.5DGISmodel,the building-stockdescriptionreflectstheheterogeneityofthebuilding-stock.Theproposedmethodisvalidatedusing alocalbuildingportfolio(N=433)inthecityofGothenburg,wheremodelledresultsforspaceheating anddomestichotwaterarecomparedtodatafrommeasurements,bothonanindividualbuildinglevel andfortheentireportfolio.Calculatedenergyusebasedonthebuilding-stockdescriptionoftheportfolio differlessthan3%frommeasuredvalues,with42%oftheindividualbuildingsbeingwithina20%margin ofmeasuredenergyuseindicatingfurtherworkisneededtoreduceorquantifytheuncertaintyona buildinglevel.

1. Introduction

Buildingsworldwideaccountfor32%oftheglobalﬁnalenergy

useand19%ofenergy-relatedgreenhouse-gas(GHG)emissions.

Atthesametime,theyprovidealargepotentialforcost-efﬁcient

energyefﬁciencymeasures(EEM)[1–4].Withtheaimtoharvest

theseefﬁciencyopportunities,theEuropeanEnergyPerformance

ofBuildingsDirectivedeﬁnesasetofefﬁciencystandardsforboth

newandexistingbuildings[5,6].Onanationallevel,Swedish

gov-ernmentalpolicyaimsatconsiderablereductionsinenergyuseby

2020and2050[7,8].ResultingfromtheEUenergyefﬁciency

direc-tive[5],energyperformancecertiﬁcates(EPC)wereintroducedin

Swedenin2006in ordertopromoteenergyefﬁciencyin

build-ings.Onalocallevel,citiesandmunicipalitieshavegonefurther

andvoluntarilyadoptedmoreambitioustargetsonenergysavings,

reductionsinGHGemissionsand increase inrenewable energy

∗ Correspondingauthor.

E-mailaddresses:magost@chalmers.se,magnus.osterbring@ncc.se

(M.Österbring).

throughframeworkssuchastheCovenantofMayors.1_The_city_of

Gothenburghasadoptedsuchgoalsandaimstoreduceenergy

con-sumptioninresidentialbuildingsby30%by2020comparedto1995

levels[9].Forthedevelopedworld,itisestimatedthat80%ofthe

buildingstockin2050willconsistofbuildingsalreadybuilt[10],

whichimpliesaneedforapplicationofEEMintheexistingstockif

theabove-mentionedtargetsaretobemet.

Thereare manyexamplesof building-stockmodelling(BSM)

beingusedtoevaluatetheenergydemandoftheexisting

building-stock[11–16].Accordingtotwoin-depthreviewpapersbySwan

andUgursal[17],andKavgicetal.[18],ageneraldistinctioncan

bemadebetweentop-downandbottom-upmodellingapproaches.

Top-downmodelscannotbeusedtoassesstheeffectsfrom

individ-ualEEMand,thus,havenotbeeninfocusofthiswork.Bottom-up

modelscan be divided intotwo sub-groups; Statisticalmodels

andbottom-upengineeringmodels.Statisticalmodelsuse

aggre-gateddataasinput,whichthroughregressionmethodsareused

1_The_Covenant_of_Mayors_was_launched_by_the_European_Commission_to_support

effortsbylocalauthoritiestoimplementsustainableenergypolicies.

http://dx.doi.org/10.1016/j.enbuild.2016.03.060

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toaccountforspeciﬁcend-usesbasedontheenergyconsumption

ofthedwellings.Abottom-upengineeringmodel,whichisused

inthispaper,usesaheatbalancemodeltoestimatestheenergy

consumptionforindividualbuildings.Thebuildingsusedasinput

tobottom-upmodelsaredeﬁnedbybuildingpropertiessuchas

geometry,U-values,climatedata,indoortemperatureanduseof

appliances.Thus,toapplyabottom-upengineeringmodelrequires

detailed inputdata.Duetoaim,dataavailability and

computa-tionaltime-constraintsthebuildingstockisnormallyrepresented

bysamplebuildingsorarchetypebuildings,whereitisassumed

thatsimilarbuildingswithregardtoyearofconstruction,useofthe

building,typeofheatingsystemandbuildinggeometrycanbe

rep-resentedbyanaveragebuilding.Samplebuildingsusedetaileddata

foranumberofbuildings(e.g.asobtainedfrommeasurementson

individualbuildings)combinedwithweightingfactorsderivedso

thatthesamplebuildingsreﬂecttheentirebuildingstock.Similarly,

archetypebuildingsuserepresentativetheoreticalbuildings,often

deﬁnedbyconstructionyearandthetypeoruseofthebuilding,

torepresentallbuildingswithsimilarcharacteristicstoallowfor

assessmentoftheentirestock.Thesemethodsofbuilding

descrip-tionhavebeensuccessfullyusedtocalculatethepotentialforEEM

inexistingresidentialbuilding-stocksonanationalscale[19,20]as

wellasonanurbanscale[21–23].

Recentimprovementsindataavailabilityhaveallowedgreater

focusonurbansettingsinBSMandinclude aspatialdimension

byintegratinggeo-referenceddatausinggeographicalinformation

systems(GIS)[24–26].UsingGISinBSMhasseveraladvantages:

itfacilitatesmergingofdatafromseveraldatabasesthatisoften

requiredbyengineeringmodels,itfacilitatesfurtheranalysisand

communicationbyspatiallydifferentiatingandvisualizingresults,

andﬁnallyitprovidesarepositoryforstoringandexchangingdata

throughinterconnectedurbanmodels.TheadditionofaGIS

compo-nenttoBSMhasbeencarriedouttoanalyseenergypolicyscenarios

inanurbancontext[24],toassesstheurbanheatislandeffecton

energydemand[25]aswellastoassessenvironmentalimpacts

ofbuildingstocksandpotentialforEEM[26].Whilethe

introduc-tionofGISinBSMhasallowedanincreaseinspatialresolutionand

enabledfocusonurbansettings,thedescriptionofthe

building-stockusingrepresentativebuildingshasnotbeenadaptedtotake

fulladvantageoftheimprovedspatialresolution.Themethodof

derivingandscalingadescriptionbasedonrepresentative

build-ingstoaccountfortheentirestockisbasedontheassumptionthat

buildingswithsimilaryearofconstructionandusehavesimilar

energyperformancecharacteristics.Thiscanbeproblematic,

typi-callysoforolderpartsofthestockwhereenergyrenovationshave

beenappliedtovaryingdegreewhichmayresultinsigniﬁcant

dif-ferencesintheenergyperformanceforthesametypeofbuildings

[27].

Asthescopeof theabove work[24–26] hasbeento

evalu-atetargetsandscenariosatacityordistrictlevel,representative

buildingshavebeensufﬁcient todescribethebuilding-stock.In

addition,measureddataonenergysupplythatallowfor

valida-tionandcalibrationofthebuilding-stockdescriptioniscommonly

onlyavailableforasmallnumberofbuildingsoratadistrictlevel,

oftenduetolackofdataavailabilityorduetodatanotbeing

pub-liclyavailable[17,28].Asaresult,validationandcalibrationofthe

building-stockdescriptionislimitedtolevelsofaggregationset

byavailabledata[21].Obviously,suchdescriptionsshouldonlybe

usedattheirintendedlevelofaggregation[29],whichinturncalls

forarevisedmethodofdescribingbuilding-stocksfor

stakehold-ersneedingbuildingspeciﬁcinformationsuchaspropertyportfolio

ownersandmanagers.Furthermore,theincreaseinspatial

resolu-tionprovidedbyadifferentiateddescriptionreducesuncertainties

onastocklevelasrepresentativebuildingsmayover-or

underesti-matetheenergydemandofaspeciﬁcbuilding,whichcouldresultin

inaccurateestimatesofthepotentialforenergysavingsfrom

apply-ingdifferentEEM.Inordertoincreasetheaccuracytoallowfor

prioritizingEEMforindividualbuildingswithinastock,measured

energyusemustbeknowntoallowforvalidationonabuildingor

propertylevel.Thelackofmeasuredconsumptiondataata

dis-aggregatedlevelhasbeenidentiﬁedasthesinglemostimportant

obstacleforhandlinguncertaintyinBSM[17,21,28,30,31].

Theaimofthispaperistopresentamethodologyfordescribing

urbanresidentialbuilding-stocksthatﬁtsanalysisoflocalbuilding

portfolios,i.e.whereagreaterdetailinanalysisisrequired

com-paredtomodellingabuildingstockofanentirecountryoralarge

region.Thus,theaimistoincludeaspatialresolutionofindividual

buildingsbyusingbuilding-speciﬁcdataandtoassessdataneeds

and,inparticulargeo-referenceddata,forsuchadescription.

2. Methodology

Building-speciﬁcdata,envelopeareaandmeasuredenergyuse

fromEPCarelinkedtoeachindividualbuildingusingGIStoachieve

adifferentiateddescriptionofthebuilding-stockofGothenburg.

Preparationandcleaningofthedataaredoneandpreviously

iden-tiﬁedqualityissuesregardinghowtheheatedﬂoorarea(HFA)is

derivedintheSwedishEPC[32,33]arecorrected.AstheU-valuefor

thebuildingsarenotknownfromanyofthedatasets,anage-type

classiﬁcationisdevelopedbasedonhistoricalbuildingregulations

and a historic construction and architectural classiﬁcation [34]

andlinkedtoindividualbuildings.Theproposedmethodologyis

appliedtoapropertymanager’sregionalportfolioofbuildingsin

thecityofGothenburg,consistingof433multi-familydwellings2

withatotalHFAof1millionm2_.

Theenergyperformanceofeachbuildingintheportfoliois

cal-culatedwiththebuilding-stockmodelECCABS[35]and,tovalidate

thedifferentiateddescriptionofthebuilding-stock,themodelled

energyuseforspaceheating(SH)anddomestichotwater(DHW)is

comparedtomeasuredenergyconsumptiondatawhichisbasedon

billingdataormeasuredonsite.Finally,asensitivityanalysisforthe

mostrelevantinputparametersconsidereduncertainisperformed.

2.1. Building-speciﬁcdatasetsandprocessing

The building-speciﬁc data used in this work are retrieved

from(a)EPC,(b)GISdataand(c)thepropertyregister.Building

characteristicsfromEPCincludetypeofheating,ventilationand

air-conditioning (HVAC)systems, number ofstories (aboveand

belowground),numberofstaircases,attachmenttoother

build-ings(detached,semi-detachedorattached),constructionyearand

HFA.Furthermore,measuredenergyconsumptionvaluesaregiven

fornon-domesticelectricityuse,SHandDHW.AllEPCavailablefor

thecityofGothenburgwereretrievedfromtheNationalBoardof

Housing,BuildingandPlanning.

BuildingcharacteristicsfromtheGISdataaregeometricaldata

suchasbuildingfootprintsandbuildingheight.TheCityPlanning

OfﬁceofGothenburgsuppliedtheGIS-data.AstheEPCand

GIS-datadonotcontainanycommonuniqueidentiﬁer,datafromthe

propertyregisterwerecollectedfromtheSwedishNationalLand

SurveytomatchthebuildingorpropertyIDfromEPCtomid-point

coordinatesofbuildings.Thiswasthenspatiallylinkedtobuilding

footprintsthatwereextrudedtocreatethe2.5Drepresentation

showninFig.1,whichisusedtoretrievetheenvelopeareaofeach

building.Thepropertyregisteralsocontainsinformationonyearof

refurbishmentandvalueyear,whichisanestimateofequivalent

agefortaxationpurposescalculatedbasedonyearofconstruction

andyearofrefurbishment[36].Thevalueyearisweightedbased

2_A_multi-family_dwelling_is_for_the_purpose_of_this_paper_deﬁned_as_a_building

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Fig.1. 2.5DrepresentationofcentralGothenburg.BuildingsinwhitehaveanEPC.

ontheeconomicextentofrefurbishmentactivitiesorextensions

andrelatestotheexpectedremaininglifetimeofeachbuilding.

2.2. Errorinvestigationandpreparationofinputdataset

ThemostcommonlycitedsourceoferrorintheSwedishEPC

istheHFA[32,33].TheHFAisrarelymeasuredintheEPCandis

insteadobtainedbymultiplyingthelivingareaby1.15forbuildings

withoutbasementandby1.25forbuildingswithabasement.Yet,

thisproceduregivessomewhatinaccuratepredictionsoftheHFA,

particularlyintwocases.1.ForbuildingswhereallHFAequalsthe

livingarea,theresultingHFAisoverestimatedby15%intheEPC.

Thisistypicalfortwo-storybuildingswithexternalstaircasesand

individualexternalentriestoallapartments.2.Forbuildingswith

abasement.AstheHFAisderivedbymultiplyingthelivingarea

by1.25,lessthanhalfofthebasementwouldbeaccountedforina

two-storybuildingassumingtheareaofthebasementequalsthat

oftheﬂoorsaboveground.Forbuildingswiththreeormorestories,

theerrorisless.

Otherissuesintheparticularsubsetof433EPCinclude

coor-dinateerrors(infourinstances)andwrongnumberofstories(in

76instances).Thecoordinateerrorsalwaysoccurinpairsandis

aresultoftwobuildingIDsforthesamepropertybeingmixedin

theEPC.Thewrongnumberofstoriesislikelyduetomanualinput

errororpartlysubmergedbasementsbeingcountedtwiceasitis

givenseparatelyforbasementsaboveandbelowgroundintheEPC.

Fortheﬁnalinputdataset,thecorrectnumberofstorieshasbeen

added,theHFArecalculatedforbuildingswithabasementor

exter-nalstaircasesandthefourinstanceswithjumbledcoordinateshave

beencorrectedmanually.

2.3. U-valuesandbuildingtype-agedescription

Table1liststheaverageU-valuesforthedifferentbuildingtypes

employedaccordingtoconstructionperiod.TheU-valueof

build-ingcomponentsisnotgiveninanyofthedatasetsandanaverage

U-valueisinsteadderivedbasedonbuildingtypeandconstruction

year.Basedonarchitecturalhistory[34]andbuildingregulations,

themulti-familydwellingstockofGothenburgischaracterizedby

sevenbuildingtypes(timberbuildings,brickbuildings,socalled

“Landshövdingehus”,3_slab_blocks,_tower_blocks,_large_slab_blocks

and courtyardbuildings)and further dividedby age,according

totheiryearofconstruction,byﬁveyearperiodsfrom1880and

onwards.Theﬁve-yeardivisionisdeterminedbytherecurrenceof

thearchitecturaltypes.Outofatheoretical196combinationsof

agegroupandbuildingtype,32uniqueaverageU-valuesforthe

3 _This_is_a_three_storey_early_to_mid_20th_century_building_type,_{characteristic}_for

theCityofGöteborg,forwhichthebottomstoreyismadeofbrickandthetoptwo ofwood.

age-typeclassesareusedasnotalltypesarepresentforallage

periodsandinmanycasestheU-valuedoesnotchangeforlonger

periods(seeTable1).

2.4. Matchingthebuildingage-typeclassiﬁcationtoindividual

buildings

Inthedatabasesmentionedabove(a–c)thereisnosingle

iden-tiﬁertomatchtheage-typeclassiﬁcationtoindividualbuildings,

insteadthenumberofstories,age,numberofstaircasesand

attach-menttootherbuildingsareusedtomatcheachbuildingtoaverage

U-valueswhichcanbeseeninTable2.Forbuildingsforwhichthe

yearofrefurbishmentisdocumented,theU-valueisbasedonthe

valueyearratherthanconstructionyear.Whiletheactual

refur-bishmentactivitiestakenarenotknown,thevalueyearatleast

givesanindicationoftheeconomicextentoftherefurbishment

andprovidesamorereasonableassumptionthanusingtheyearof

refurbishmentorconstructionyearwhenestimatingtheU-value.

IncasesforwhichthevalueyearhasnoU-valuespeciﬁedforthe

buildingtype,theaverageU-valueisbasedonotherbuildingtypes

forthetime-periodinquestion.Wherethereisanoverlapof

possi-blebuildingtypes,towerblockischosenasitbydeﬁnitionexcludes

anyotherpossibilities.Ofthe32age-typeclassesgiveninTable1,

22areusedtoassignanaverageU-valueforthe433buildings.

Thisissufﬁcientsinceolderbuildingsarerareintheportfolioof

buildings,withonlysevenbuiltbefore1940.

2.5. Assumptionsfornon-buildingspeciﬁcinputs

Asbuilding-speciﬁcinputdataarenotavailableforall

parame-tersrequiredformodellingtheenergydemand,i.e.notpresentin

EPCorgeometricaldata,remaininginputdatahavebeenacquired

usingassumptionsthat aremoregeneral.In particular:internal

heatgains,userbehaviourrelatedinputsandset-point

tempera-ture(21◦C)arespeciﬁedaccordingtonationalbuildingindustry

standardsforenergycalculations[38],whileventilationﬂowrates

areassumedtofulﬁlminimumhygienicstandardsaccordingtothe

buildingcode[39].Forbuildingsusingheatpumps(49buildings

ofthe433intheportfolio),thecoefﬁcientofperformance(COP)

isassumedaccordingtotestsperformedbytheSwedishenergy

agency[40].

2.6. Validationbyenergymodellingofthebuildingstock

Inordertovalidatethedifferentiateddescriptionofthebuilding

stock,theenergyuseforSHandDHWiscalculatedusingthedeﬁned

descriptionasinputtotheECCABSmodel[35]andcomparedto

measuredenergyconsumptiondataonaportfoliolevelaswellas

onabuilding-by-buildingbasis.Althoughmeasureddataaregiven

separatelyforSHandDHWintheEPC,theyarenotmeasured

sep-aratelyforanyofthebuildingsintheportfolio,butratherdivided

basedonthejudgementoftheenergyexpertperformingtheEPC.As

such,resultsarepresentedasthesumofcalculatedandmeasured

energyuseforSHandDHW.Inaddition,asensitivityanalysisis

performedforparametersthatareuncertainandinﬂuential,similar

towhathasbeenidentiﬁedinpreviouswork[31,41].Asindicated

above,theenergyperformanceoftheportfolioismodelledwiththe

ECCABSmodel[37],wheretheenergyperformanceofeachbuilding

ismodelledmathematicallyinSimulink/Matlab,asalinearexplicit

discretetime-variantsystem,basedonthelumpedsystemanalysis

approach.Accordingtotheclassiﬁcationofcalculationprocedures

inISO13790[42],theECCABSmodelisdetailed(hourly

speciﬁ-cationoftheinputdataandtheresults)anddynamic(takesinto

accountthethermalmassofthebuildingateachtimestep).The

accuracyoftheECCABSmodelhasbeenvalidatedbyinter-model

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Table1

AverageU-valuesforthedifferentbuildingtypesaccordingtoconstructionperiod[W/m2_,_K].

Constructionperiod Timberbuilding Brickbuilding Landshövdingehus Slabblock Towerblock Largeslabblock Courtyardbuilding

–1905 1.10 1.20 1.00 1905–1920 1.05 1.20 1.00 1920–1930 0.95 1.20 1.00 1.35 1930–1935 0.95 1.20 1.00 1.00 1935–1940 0.95 1.20 1.00 1.00 1.10 1940–1950 0.95 0.95 1.10 1950–1955 0.95 1.10 1.00 1955–1960 0.90 1.10 1.00 1960–1965 0.70 1.25 0.95 1965–1970 0.70 1.25 0.90 1970–1975 0.70 0.85 0.90 1975–1980 0.70 0.75 0.80 0.50 1980–1985 0.60 0.60 0.60 0.50 1985–2000 0.55 0.55 0.55 0.50 2000–2010 0.50 0.50 0.50 0.50 2010–2015 0.4 0.4 0.4 0.4 Table2

Propertiesformatchingtheage-typeclassiﬁcationinTable1toeachbuildingintheportfolio.

Buildingtype Attachment Stories Staircases Constructionperiod

Towerblock Detached >2 1 1935–2015

Landshövdingehus Attached/semidetached 3 1+ 1880–1940

Brickbuilding Multiple >3 Multiple –1940

Largeslabblock Detached/semidetached >4 Multiple 1950–2015

Slabblock Detached/semidetached 2–4 1+ 1930–2015

Timberbuilding Detached 2 0–1 –1950

Courtyardbuilding Attached >3 Multiple 1975–2015

tomodeltheenergyperformance oftheSwedishbuilding-stock

[43,44]aswellasthatofvariousEuropeancountries[20,45].

3. Results

In this section, calculated and measured energy use for SH

andDHWareﬁrstpresentedfortheentireportfolioandsecond

abuilding-by-buildingcomparisonisshown.Lastly,asensitivity

analysisis carried out tohighlight theinﬂuence of parameters

thatareuncertain.Fig.2comparesmodelledandmeasuredenergy

usefortheportfolio(thesumofSHandDHW).Whilemodelled

andmeasuredenergyuseonaportfolioleveldifferlessthan3%,

(modelled136GWhandmeasured139GWh),thedifferencefor

individualbuildingscanbesigniﬁcant.

Theagreementfortheportfolioissimilartowhatwasobtained

when comparingmodelledand measuredenergy demand ona

nationalbuilding-stocklevel,usingrepresentativesample

build-ings.Figs.3showsthedistributionofthemarginofmodelledto

measuredenergyusein0.1intervals,normalizedfornumberof

buildingsaswellasm2_HFA._As_can_be_seen,_there_is_a_wide

distri-butionintherationofmodelledtomeasureddatawhere52%ofthe

HFAand43%ofnumberofbuildingsarewithina20%margin.Thus,

theresultsalsoindicatethatmoreaccuratepredictionsofenergy

useareobtainedforlargerbuildingsastheaccuracyishigherwhen

comparingHFAratherthanthenumber ofbuildings.Ascanbe

seenfromFig.4,thedeviationsbecomeevenmoreapparentwhen

applyingenergyuseperm2_HFA_as_basis_for_comparison_between

modelledandmeasuredenergyuse.Itisclearthattheheating

sys-temhasasigniﬁcantimpactwherethecalculatedenergyusefor

buildingswithaheatpumpisconsistentlyunderestimated.The

oppositecanbesaidforbio-basedboilerswherecalculatedenergy

useisconsistentlyhigherthanmeasuredenergyuse.

3.1. Sensitivityanalysis

TheCOPofheatpumpsandtheindoorset-pointtemperatureare

knowntobeassociatedwiththehighestuncertaintiesofthedata

0 500 1000 1500 2000 2500 3000 3500 0 500 1000 1500 2000 2500 3000 3500 Me a su red ene rgy use for S H a n d H W [MWh/ ye a r]

Calculated energy use for SH and HW [MWh/year]

Fig.2. Calculatedandmeasuredenergyuseforindividualbuildings(thesumofSH andDHW).

usedinthisworkandthereforeasensitivityanalysisiscarriedout

forthesetwoparameters.TheCOPofheatpumpsisinvestigated

accordingtoTable3andevaluatedforeachofthe49buildingsusing

heatpumps.Outofthe49buildingswithaheatpump,ninehavea

ground-sourceheatpump,34haveanexhaust-airheatpumpand

ﬁvehaveanairtowaterheatpump.Furthermore,thesetpoint

forindoortemperatureisvariedbetween20and24◦Cwiththe

initialassumptionbeing21◦Candevaluatedataportfoliolevel,see

Fig.5.Althoughuserbehaviourhasalargeimpactonthecalculated

energyperformanceofindividualbuildings,noneofthedatainthe

databasesallowsacharacterizationoftheuserbehaviourand,thus,

ithasbeenomittedatthisstage.

Whiletheresultsfortheinitialassumptionofaset-point

tem-peratureofindoortemperatureof21◦Cshowgoodagreementwith

measuredconsumption data,varyingtheset-pointtemperature

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.91 10% 20% 30% 40% 50% 60% m² HFA , no rmalised [-] Margin of error [%] 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.91 10% 20% 30% 40% 50% 60% Number of buildi n gs, no rmalised [ -] Margin of error [%]

Fig.3.Distributionofmarginoferrorbetweencalculatedandmeasuredenergyuse(sumofSHandDHW),normalizedform2_HFA_to_the_left_and_number_of_buildings_to_the

right. 0 50 100 150 200 250 300 350 0 50 100 150 200 250 300 350

Measured

energy

use

for

SH

and

DH

W

[k

Wh/

m²,y]

Calculated

energy

use for

SH and DHW [kWh/m²,y]

District heang

Heat-pump

Gas

Bio-based boiler X=Y

Fig.4.Calculatedandmeasuredenergyuseperm2_HFA_grouped_by_heating_system.

Table3

VariationofCOPforthedifferentheat-pumps.

Heatpump InitialCOP Variation1 Variation2 Variation3

Exhaust-airheatpump 2.5 1.5 2 3

Airtowaterheatpump 2 1.5 2.5 3

Ground-sourceheatpump 3 2.5 3.5 4

0 20 40 60 80 100 120 140 160 180 20 21 22 23 24 GWh /year Set-point temperature

Fig.5. Sensitivityanalysisofchangeofset-pointtemperaturewithaline

represent-ingmeasuredenergyuseforcomparison.

theSHdemandfortheentireportfoliowithonlyminorﬂuctuations

forindividualbuildings.

Figs. 6 and 7 give calculated energy use against measured

consumptionfor the49buildings witha heat pumpwherethe

assumptiononCOPisvariedandresultsarenormalizedtoallow

forabetteroverview.Ascanbeseen,themeasuredenergy

con-sumption differs signiﬁcantly from calculated energy demand.

For ground-source heat pumps and air to water heat pumps,

thecalculated energy useis consistently underestimated while

exhaust-airheatpumpsshowamoreinconsistentbehaviour.As

canbeexpected,variationsofCOPhave amajorimpacton

cal-culatedenergy use, butas thesamplesize issmall andsimilar

variationsoccurforbuildingswithdistrictheating,itisnotclear

whethertheassumptionsonCOPareexaggeratedorifotherfactors

suchasuserbehaviourarethereason.

4. Discussion

Calculatedenergyuseshowsgoodagreementwithmeasured

energyuseonaportfolioleveland43%ofthebuildingsrepresenting

52%oftheHFAhaveamarginoferrorlessthan20%.Thereare

sev-eralpossibleexplanationsforthediscrepancybetweenmeasured

andcalculatedenergyuseonabuildinglevel.First,thereliability

ofthedatacanbequestioned,bothwhenitcomestovaluesused

asinputaswellasthemeasuredenergyconsumption statedin

theEPC.ItisclearthattheEPCinparticularsuffersfromquality

issuesandwhilethemostapparenterrorsrelatingtoinputhave

beendealtwith(HFA),therearetwomainissuesremaining

con-cerningmeasuredenergyconsumptiongivenintheEPC.1)Energy

forSHandDHWisnotmeasuredseparatelyforanyofthe

build-ingsintheportfoliobutratherseparatedbasedonthejudgement

oftheenergyexpert.2)Forsomepropertiesthatincludeseveral

buildingsofsimilartypeandsize,onlyonemeasuringpointisused

whichisthendividedbasedontheHFAofthebuildingsinquestion,

whichisthecasefor165ofthebuildings.Thisincreasestheriskthat

energyusewhichisnotincludedinthecalculationisincludedinthe

measureddatasuchasdistrictheatingdistributionlossesorother

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0 0.2 0.4 0.6 0.8 1 1 2 3 4 5 6 7 8 9 Calculated SH and DHW use

normalised against measured data

[-]

COP 2.5 COP 3 COP 3.5 COP 4

0 0.2 0.4 0.6 0.8 1 1 2 3 4 5 6 Calculated SH and DH W use norma lis ed against me asured data [-]

Fig.6.Calculatedenergyuse(sumofSHandDHW)normalizedagainstmeasuredconsumptionvaluesfordifferentassumptionsonCOPofground-sourceheatpumpson theleftandairtowaterheat-pumpsontheright.

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 Calculated SH and DH W use norma lis ed against me asured data [-]

Fig.7.Calculatedenergyuse(sumofSHandDHW)normalizedagainstmeasuredconsumptionvaluesfordifferentassumptionsonCOPofexhaust-airheatpumps.

outdoorlightingwhichtheenergyexpertwouldhavetoaccount

formanually.

Second,partofthedeviationbetweencalculatedandmeasured

valuesmayalsobearesultofthemethodologyusedinthisworkto

describethebuildings.Inparticular,therehavebeentencasesinthe

portfoliowherea2.5Dgeometryhasnotbeensufﬁcienttodescribe

thegeometryofabuilding.Forfutureworkthereisapotentialin

using3D-datatogetherwithadigitalterrainmodel(DTM)tomore

accuratelycalculatetheenvelopeareaonabuildinglevel.

Addi-tionally,whileaverageU-valueshavebeenassignedbasedonthe

age-typeclassiﬁcationandthevalueyear,usingU-valuesfor

build-ingcomponentscouldfurtherimprovetheaccuracyforindividual

buildings.

Althoughallofthebuildingsintheportfolioareclassiﬁedas

residentialbuildings,itiscommonformulti-familydwellingsto

alsohostcommercialactivities(thisisthecasefor136ofthe433

buildings,onaverage6.8%oftotalHFA),typicallyontheﬁrstﬂoor.

AstheECCABSmodelis conﬁguredasa lumpedmodel

(single-zonemodel),furtherimprovementscanbemadebyconsidering

theseactivitieswhenspecifyinginternal heatloadsor,tousea

multi-zonemodelthatcanmoreaccuratelyrepresenttheactual

conditionswiththedrawbackof additionalcomputationaltime

required.Theuseofmulti-zonemodellingforbuilding-stockshas

previouslybeeninvestigatedforsingle-familydwellingsintheUK

[46]butfurtherworkisneededtoalsoincorporatemulti-family

dwellingswithmixeduse.

Theparameterschosenforthesensitivityanalysisissimilarto

previousworkthatusesreferencebuildings[41]aswellassample

buildingsandafactorialsamplinganalysisasabasisforcalibration

[31].ItisclearlyshownthatindoortemperatureandtheCOPofheat

pumpshaveamajorimpactonresultsandfurtherworkisneeded

toovercometheseuncertainties.Whilenotaddressedinthispaper,

theuncertaintyofuserbehaviourandU-valuesbasedonvalueyear

requires furtherattention.Previous workhasconcluded thatin

ordertobetterunderstand,communicateanddescribe

uncertain-ties,localorbuildingspeciﬁcconsumptiondataareneeded[47].

Althoughthedatasetusedinthisstudyisuniqueinthesensethat

itcontains measuredenergyconsumptiondataonabuildingor

propertylevel,futureimprovementsofhowitisutilizedcouldbe

accomplishedbyintegratingaBayesiancalibrationframework

sim-ilartowhathasbeensuccessfullyimplementedpreviously[31,48].

5. Conclusions

Amethodologyhasbeenpresentedfordescribingurban

resi-dentialbuilding-stocksthatallowsaspatialresolutionofindividual

buildingsbyusingbuildingspeciﬁcdata.Themethodologyconsists

ofusingbuilding-speciﬁcdatafromtheEPCandenvelopeareas

froma2.5DGIS-modeltoaugmentanage-typeclassiﬁcation.The

descriptionis validatedbycomparingmodelleddataonenergy

use(thesumofSHandDHW)withmeasureddatafroma

build-ingportfolioof433buildingsinthecityofGothenburg.Whilethe

descriptionisabletoreﬂectmeasuredenergyuseonaportfolio

levelwithcalculatedenergyusebeingwithin3%,furtherworkis

neededtoreduceorquantifytheuncertaintyonabuildinglevel

as43%ofbuildingfallswithina20%marginoferror.Tothisend,

dataneedsandavailabilityarenotseenasthemainhindrancefor

improvingtheaccuracyonabuildinglevelwiththeexceptionfor

SHandDHWnotbeingmeasuredseparatelyintheEPC. Rather,

furtherimprovementsofthemethodologycanbemadetoincrease

theaccuracyonabuildinglevel,speciﬁcallybyforgoingaverage

(7)

addition,calibrationmethods couldbeincorporatedtoquantify

uncertainties.Futureresearchareashavebeenidentiﬁed,

explic-itlythemajorimpactfactorsforthevariationofCOPofdifferent

typesofheatpumps.

Acknowledgements

ThisresearchhasbeenfundedbytheSwedishenergyagency,

NCCAB,theClimate-KICandChalmersEnergyAreaofAdvance.

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