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
Using a segmented dynamic dwelling stock model for scenario analysis of future energy demand: The dwelling stock of Norway 2016–2050
Nina Holck Sandberg
a,∗, Igor Sartori
b, Magnus I. Vestrum
a, Helge Brattebø
aaIndustrialEcologyProgramandDepartmentofEnergyandProcessEngineering,NorwegianUniversityofScienceandTechnology(NTNU),7491 Trondheim,Norway
bSINTEF,DepartmentofBuildingandInfrastructure,P.O.Box124Blindern,0314Oslo,Norway
a r t i c l e i n f o
Articlehistory:
Received20September2016
Receivedinrevisedform24March2017 Accepted6April2017
Availableonline13April2017 Keywords:
Buildingstocks Dynamicmodelling Energyanalysis Scenarioanalysis
a b s t r a c t
Thehousingsectorisimportantforfutureenergysavingsandgreenhousegasemissionmitigation.A dynamic,stock-drivenandsegmenteddwellingstockmodelisappliedfordwellingstockenergyanalyses.
Renovationactivityisestimatedastheneedforrenovationduringtheageingprocessofthestock,in contrasttoexogenouslydefinedandoftenunrealisticrenovationratesappliedinothermodels.Thecase studyofNorway2016–2050showsthatdespitestockgrowth,thetotaltheoreticalestimateddelivered energyisexpectedtodecreasefrom2016to2050by23%(baseline)and52%(mostoptimisticscenario).A largeshareoftheenergy-efficiencypotentialofthestockisalreadyrealizedthroughstandardrenovation.
Thepotentialforfurtherreductionsthroughmoreadvancedand/ormorefrequentrenovation,compared tocurrentpractice,issurprisinglylimited.However,extensiveuseofheatpumpsandphotovoltaicswill givelargeadditionalfutureenergysavings.Finally,userbehaviourishighlyimportant.Astrongfuture reboundeffectisexpectedasthedwellingstockbecomesmoreenergyefficient.Theestimatedtotal‘real’
energydemandisexpectedtodecreasebyonly1%(baseline)and36%(mostoptimisticscenario).Hence, reachingsignificantfutureenergyandemissionreductionsintheNorwegiandwellingstocksystemwill bechallenging.
©2017TheAuthors.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction
1.1. Backgroundandcontext
Residentialbuildingsareresponsiblefor24%oftheglobalfinal energyconsumptionandthebuildingsectorisimportantforfuture mitigationofgreenhousegas(GHG)emissions[1,2].Energyanal- ysesandscenariomodelsareimportanttoolsforquantifyingthe energysavingpotentialsofthestock,andpoliticalroadmapsand actionplansshouldbeusedtoensurethatasmuchaspossibleof thepotentialsavingswillbeobtained.Scenarioanalysisoffuture buildingenergydemand canreveal discrepancies, uncertainties andpriorityareasofimprovements,aswellashighlighttheneed forimproveddatacollection[3].
∗Correspondingauthor.
E-mailaddresses:[email protected](N.H.Sandberg),
[email protected](I.Sartori),[email protected](M.I.Vestrum), [email protected](H.Brattebø).
Tofacilitatetheimplementationofsuccessfulclimate-change mitigationpolicies,itiscrucialtobetterunderstandthedynamic andcomplexnatureofthefuturebuildingstockenergysystem.
Theenergydemandofa dwellingstockdepends on(i)thesize andcompositionofthestock,(ii)theenergy-efficiencystateofthe buildings,(iii)outdoorclimate,(iv)theenergymixandefficiencies oftheenergydistributionandconversiontechnologies,(v)theuse oflocalenergysourcesand(vi)theuserbehaviour.Allthesefactors willchangeovertime,andthetemporalchangesmustbeexamined inscenarioanalyses.Tounderstandtheinfluencesofthelong-term transformationofadwellingstock,thereisalsoaneedtoquantify andanalysetherobustnessofkeydata,fromretrofittingratesto totalstockenergyeffects,aswellastheassociatedassumptions [4].
1.2. Dwellingstockenergymodelsappliedinliterature
Buildingstockenergymodelsarecommonlyclassifiedaseither
‘top-down’or‘bottom-up’.However,amorerefinedclassification systemisrequiredtobetterunderstandthequalitiesandapplicabil-
http://dx.doi.org/10.1016/j.enbuild.2017.04.016
0378-7788/©2017TheAuthors.PublishedbyElsevierB.V.ThisisanopenaccessarticleundertheCCBY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4.
0/).
ityofthemodels.Adetailedreviewofexistingmodelsispresented inVasquezetal.[5],andclassifiedbymodellingdimensionsand approachesaccordingtomaterialflowanalysis(MFA).
Accountingmodelsmainlyquantify stocksize and composi- tion, and associated material or energy flows (e.g. [6–8]).This type ofmodels is basedonaccounting principlesand doesnot intendtoanalysethedriversofstockdevelopmentandenergyuse.
Quasi-stationaryanddynamicmodellingapproachesmakeuseof differentdriverstoexplainthesize,composition,andenergycon- sumptionofthestock.Quasi-stationarymodelscommonlystudy thebuildingstockforonesingleyear(e.g.[9–11]).Dynamicbuild- ingstockenergymodelsanalysechangesovermultipleyears.Asin Vasquezetal.[5],weclassifydynamicmodelsaseither(a)input- oractivity-driven,or(b)stock-driven.Dwellingstockenergymod- elsusingdynamicmodelsarepresentedinTable1,wheretheyare classifiedbythemodellingdimensionsandapproaches.
Activity-drivenmodelsgenerallyuseconstructionanddemoli- tionratesasdrivers.Theactivity-drivendynamicmodelspresented inTable1mostlyapplyconstructionanddemolitionratesthatare basedonrecenttrends,whereastheenergyanalysisisoftencon- ductedinhighdetail.However,therealismoftheappliedrates ortheresultingsimulatedfutureevolutionofthestockisnotdis- cussed in thesepapers. Furthermore,severalstudies showthat theresultsofbuildingstockenergyscenarioanalysesarehighly dependentontheappliedrenovationrate.Thisrateisoftenbased onexogenouslydefinedassumptionswithlittleevaluationofthe actualrealismoftheappliedrate.Thesestudiescommonlycon- cludeonlargepossibleenergysavingsthroughenergyefficientnew constructionandrenovation.However,impliedinthisisrenova- tionratesincreasingrapidlytolevelsofe.g.2.3–3%by2030,and thelikelihoodofthisreallyhappeningisrarelydiscussed.
Stock-drivenmodelsusetheservicedemand/provisionconcept introducedtodwellingstockmodellingbyMüller[34].Thiscon- ceptreliesontime-changingfactorslikepopulationandlifestyle.
Mass-balanceprinciplesareusedtomodelconstructionactivity, sothatnewconstructionsatisfieschangesindemandandaccounts fordemolishedbuildings.Demolitionactivityismodelledeitherby useofafixeddemolitionrate[28,30,32],aleachingmodel[31],or ademolitionprobabilityfunction[5,27,29,33].
Thestock-drivenenergymodelspresentedinTable1arestock drivenastheturnoverofthestockisestimatedbasedonthechang- ingdwellingstock demand.However,therenovation activityis mostlymodelledbyuseofexogenouslydefinedrenovationrates.
Infact,themodelsarethereforehybridmodelsastheconstruc- tionanddemolitionactivityareestimatedusingthestock-driven model,while therenovation activityisactivitybased. Theonly exceptionisourpreviousstudy[33]whererenovationactivityis estimatedbyuseofarenovationprobabilityfunction.
Intheirstock-drivenmodeldescribingthelong-termdynamics oftheNorwegiandwellingstock,Sartorietal.[35]makethefirst useofrenovationprobabilityfunctions.Therenovationactivityis thenalsoestimatedinternallyinthemodel,accordingtothestock- drivenmodellingprinciples.Thismakesitpossibletoestimatethe
‘natural’needforrenovation,resultingfromtheageingprocessof thestock.
Themodel fromSartoriet al.[35] wasfurtherdeveloped in Sandberg etal. [36], wherethe dwellingstock issegmented in dwellingtypesandconstructionperiods(cohorts).Thedistribution ofthestocktosegmentsmakesitpossibletokeeptrackofhowthe stock’scompositionischangingovertime.Modellingtherenova- tionactivitybyuseofaprobabilityfunctionallowsusingrealistic estimatesfortherenovationactivity,accordingtothebestavailable information.Thesimulatedenergyrefurbishmentfrequencythere- forefollowsthe‘natural’renovationactivityinthesystem,based onthebest available information. Thissegmented and entirely dynamicdwellingstockmodelcanbeappliedfordetailedanaly-
sesofhowadwellingstock’senergydemandischangingovertime.
Thesegmentationofthestockandtheinternalmodellingofrenova- tionactivitytogethermakesitpossibletoapplyenergyintensities definedforeachdwellingtype,cohortandrenovationstatecom- bination.Thiscanbeusedtodescribeindetail,insidethemodel, howtheenergydemandofthestockischangingovertime.Thisis animportantdifferencefromourfirstandsimplifiedenergyanal- ysescarriedoutinSandbergetal.[27]andSandbergandBrattebø [29].
Thesegmenteddynamicdwellingstockmodelwasfirstapplied for energy analysesin our studyof the historical development (1960–2015) ofthe energydemand in theNorwegiandwelling stockinSandbergetal.[33].There,thedwellingstockmodelis combinedwithsegment-specific energyintensitiesfroma Nor- wegianresidentialbuildingtypology databasedeveloped in the IEE-EPISCOPE project [37]. Five important factors are found to haveinfluencedtheaggregatedhistoricalenergydemandinthe stock.Energy-efficiencyimprovementsinthebuildingenvelopes, throughnewconstructionandrenovationactivityare−asexpected
− foundtosignificantly slow downthegrowthin total energy useover theperiod. More surprisingly, the effects of changing energymixandimprovedheatingsystemefficienciesareinthe sameorderofmagnitudeastheeffectsof improvementsin the buildingenvelopes.Further,increasingoutdoortemperaturesover theperiodhasreducedtheenergydemandsignificantlycompared toahypotheticsituationwithconstantclimate.Changesinuser behaviourhave,however,ledtomuchhigherenergydemandthan theunchanged1960behaviourwouldsuggest.Thisincludesboth changingheatinghabitsaslargersharesofthehousesareheated tohigher temperatures,and a doublingin theelectricloadper dwellingduringtheperiodunderstudy.
1.3. Hypothesisandresearchquestions
Themainlessonslearnedfromthehistoricalanalysisarethatthe Norwegiandwellingstockenergyusesystemwentthroughsignif- icantandimportantchangesoverthepastdecades,asdescribed above,andthatadynamicsegmentedbottom-upmodelisneces- sarytoexaminethecause-effectrelationshipsinthisdevelopment.
The model should therefore alsobe used for scenario analysis offuturelikely development.Thenoveltyofthisdwellingstock energymodelistheuseofadynamic,multi-typeandmulti-cohort stock-drivenmodel,whereannualrenovationactivity,inaddition toannualconstructionanddemolitionactivity,isestimatedwithin themodelasafunctionofthechangingstocksizeandcomposi- tion.Toourknowledge,noexistingforecastingmodelshaveapplied suchastock-drivenandmass-balancebaseddynamicmethodology forenergyanalysesofdwellingstocks.
Thepresentstudyisafollow-uptoourhistoricalanalysis[33]
andusesthesamemethodologyforenergyanalysesforthefuture Norwegiandwellingstocktowards2050.Ourhypothesisisthatby introducingcurrentlyavailabletechnologyinrefurbishedandnew buildings,itwillbepossibletoreducetheenergydemandinthe Norwegiandwellingstockbysome50%by2050,despitestrong stockgrowth.
Mainresearchquestionsare
i)HowistheNorwegiandwelling stockexpectedtoevolvein termsofsize,compositionofbuildingtype-agesegmentsand energyefficiencystandardasaresultoffuturerenovation,new builtanddemolitionactivities?
ii)Whatarethepotentialsforenergysavingsinthesystem?
iii)Whatistherelativeandcombinedimportanceofdifferentphe- nomena:a)improvedenergyefficiencyofthestockduetomore
Table1
Dynamicmodelsandstudiesforenergyuseinbuildingstocks:classificationbymodellingdimensionsandapproaches.
Modelapproach Onetype,onecohort Multipletypes,onecohort Multipletypes,multiplecohorts
Activitydriven Ozturketal.[12] Sartorietal.[13] Kohleretal.[14]
Lavenergiutvalget[15] Heerenetal.[16] Palmeretal.[17]
TheClimateandPollutionAgency[18] Onatetal.[19] Petersdorffetal.[20]
BuildingsPerformanceInstituteEurope[21] U.S.EnergyInformationAdministration[22]
Silleretal.[23]
Hensetal.[24]
McKennaetal.[25]
ReynaandChester[26]
Stockdriven Sandbergetal.[27] GlobalBuildingsPerformanceNetwork[28]
SandbergandBrattebø[29] Boermansetal.[30]
Pauliuketal.[31]
Bettgenhäuser[32]
Vasquezetal.[5]
Sandbergetal.[33]
ambitiousandfrequentrenovationmeasures,b)increaseduse oflocalrenewableenergysources,andc)likelyreboundeffects offutureuserbehaviour?
2. Methodology 2.1. Analyticalmethods
The conceptualoutline of thesegmented dynamic dwelling stockmodelanditsapplicationforenergyanalysesispresented inFig.1.Thisshowshowdifferentvariablesarerelatedtoeach other.Themainprinciplesofthemodelareexplainedbelow,and themathematicalframeworkispresentedinAppendixA.Further descriptionofthemodelanditsalgorithmisofferedinprevious publications[36,38].
Themodelconsistsoftwoparts,wherethefirstpartisthebuild- ingstockmodelandthesecondisthebuildingstockenergymodel.
Thecoreofthedwellingstockmodelisthepopulation’sdemand fordwellings.Inputstothemodelaretimeseriesforthehistorical andfuturedevelopmentofthepopulationandthelifestyleparam- eterpersonsperdwelling.Fromthis,thesizeandcompositionof thedwellingstockisestimatedforallyears.Longtimeseriesare neededduetothelonglifetimeofbuildings.Thedwellingstockis distributedtosegmentsaccordingtothedwellingtypesandcon- structionperiods(cohorts).
Demolitionactivityisestimatedbyapplyingademolitionprob- ability function to the construction activity from all previous years.Furthermore,mass-balanceprinciplesareusedtoestimate construction activity as what is needed to replace demolished dwellingsandmeetnetchangeindemand.
Finally,themodelaimsatestimatingthe‘natural’renovation rate,resultingfromtheneedforrenovationduringtheageingpro- cessofthedwellings.Acyclicrenovationprobabilityfunctionis appliedtoconstruction activityfromallpreviousyears toesti- matetherenovationactivityinthesystem.Whiledemolitionof adwellingcanhappenonlyonce,renovationcanhappenseveral timesduringabuilding’slifetime.Theaveragetimebetweenren- ovationsofacertaintypeiscasespecificandisdefinedaccording tothetypeofrenovationunderstudy.Therenovationactivitydoes notaffectthestock’ssizeoragecomposition.
Theenergy-efficiencystateofadwellingcanbesubstantially improvedwhen thedwellingis renovated.Inthemodel,this is implementedbytheuseofarchetypes.Eachsegment,definedby dwellingtypeandcohort,isdividedintothreearchetypesaccording tothreerenovationperiodsthatrefertoifandwhenthedwelling waslastrenovated.Dwellingsbeingintheiroriginalstateareplaced inarchetype1.Ifa dwellingwasrenovatedsofarbackin time, duringrenovationperiod1,thattherenovationwasassumednot tosignificantly affecttheenergy-efficiency stateof thebuilding
envelope,thedwellingremainedinarchetype1.Fromthestart ofrenovationperiod2,energy-efficiencymeasuresareassumed tobecommonlyintroducedduringdwellingrenovation.Hence, dwellingsgoing throughrenovation in renovation period 2 are movedfromarchetype1toarchetype2.Futurerenovation,inren- ovationperiod3,characterizedbypossibleevenmoreimproved energy-efficiencymeasures,makesdwellingsmovetoarchetype3.
In the building stock energy model, the stock composition in number of dwellings per archetype and year is combined withsegment-specificaverageheatedfloorareaperdwellingand bottom-updeterminedarchetype-specificenergyparameters:i.e.
energyneedintensities,contributionfromlocalenergysources, weightedaverageefficiencyofheatingsystemsandenergymix.
Thisisusedtoestimatetheenergyneed,deliveredenergyanduse ofdifferentenergycarriersforallyears,eitherforthetotalstock, perdwellingtype,cohort,segmentorarchetype.Theenergyneed dependsonthetechnicalstandardofthebuildingenvelopeandis theamountofenergyneededforspaceheatingorcooling,domes- tichotwater,ventilationandelectricalappliances.Thedelivered energyis theamountofenergy thathastobedeliveredtothe dwellingtofulfiltheenergyneedafteraccountingforonsiteenergy generationandlossesintheheatingsystem.
Thetechnicalestimatedenergy demandmaydiffer fromthe realenergyconsumption.Inbuildingsofpoorenergyquality,the realenergyuseis commonlylowerthanthetechnical estimate assmallersharesofthebuildingisheated tothetemperatures assumedinthetechnicalestimate.Thisiscalleda‘preboundeffect’
[39,40].Ontheotherhand,a‘reboundeffect’isobservedwhena highlyenergy-efficientdwellingisheatedtohighertemperatures thanassumedinthetechnicalestimate,duetothecomfortfactor andthelowadditionalcostofincreasedtemperatures[39–41].The realenergysavingafterrenovationmightbesignificantlylower than the technical energy-saving potential [40]. In the present model,athermaladaptationfactorisappliedtothetechnicalesti- matedenergydemandofthedwellingstock,tocorrectforpossible preboundandrebound effectsand toestimatethe‘real’energy demand.
2.2. DataandassumptionsfortheNorwegiancase
The presented model is generic and can be applied to any dwellingstockandtoanytimeperiod.Inthefollowing,wepresent ascenarioanalysisforfuturedevelopmentinenergydemandinthe Norwegiandwellingstocktowards2050.Dataandassumptionsare presentedbrieflybelowandindepthinAppendixB.Asummaryof theparametervaluesoftheinputdataintheyears2016and2050is giveninTable2,withdatasourcesandunderlyingassumptionsand followedbyexplanationsofimportantdefinitionsandassumptions appliedinthestudy.
Fig.1. Conceptualoutlineofthebuildingstockmodelandthebuildingstockenergymodel.Hexagonsrepresentinputvariables,rectanglesrepresentstocksandovals representflows.Allinputsandoutputsaretime-dependent.
Thecombinationofincreasingpopulationanddecreasingnum- berofpersonsperdwellingsleadstoagrowingstock.Thelifetime probabilityfunctionisassumedtofollowaWeibulldistribution withanaveragelifetimeofdwellingsof125years.
Thedefinitionoftherenovationactivityiscase-specificinthe model.Inthisstudy,weexplorethedynamicsofrenovationsof buildingenvelopesthathavethepotentialforincludingenergy- efficiency measures leading to a large decrease in the energy demand.Theimplementationofthesemeasuresarecostlyandnot likelytotakeplaceifadwellingisnotgoingthrougharenovation forotherreasonsanyway.Hence,suchmeasurescouldbeimple- mentedwhenthedwellingisrenovatedbecauseofa‘natural’need formaintenanceandupgradingduetoitsageingprocess.Inthis study,weassumedeeprenovationoffacadestooccurinrenova- tioncyclesof40years,andweestimatethetotalrenovationactivity resultingfromthisageingprocessofthedwellingstockinNorway.
Thesechangesinthebuildingenvelopesaffecttheenergyneedof thedwellingstock.
ThedwellingtypesSingleFamilyHouse(SFH),TerracedHouse (TH)andMultiFamilyHouse(MFH)and9cohortsareappliedin theanalysis.ThecohortsaredefinedinTable3togetherwiththe heatedfloorareaperdwellingineachsegment.Energyneedinten- sitiespertype/cohort/renovationvariantaretakenfromtheIEE
researchprojectEPISCOPE[54],whereasetofbuildingsrepresent- ingtype/cohortcombinationsinthenationaldwellingstocksofa rangeofEuropeancountrieshasbeendescribedindetail.Thetech- nicalstandardincludingenergyintensitieshasbeendescribedin detailforeachbuildingintheiroriginalstate,forastandardrenova- tionaswellasanadvancedrenovation.IntheNorwegiantypology, theenergyneedintensitiesdecreasefromoldercohortstonewer (up to 258kWh/m2 in original state for dwellings constructed before1955and35kWh/m2infuturedwellingsconstructedafter 2020)aswellasfromoriginalstatetostandardoradvancedreno- vation(decreasingbyupto50%and75%,respectively)[37].
Thearchetypesaredefinedbythedwellingtype, cohortand renovation period.Theactualrenovation variant(originalstate, standardrenovationoradvancedrenovation)anditscorrespond- ingenergyneedintensity,chosenfor eachrenovationperiod,is definedaspartofscenarioassumptions.
Technologychangesinthebuildingenvelopesaffecttheenergy needinthesystem.Inparallel,wemayhavechangesinenergymix andincreaseduseoflocalenergysources,whichaffecttheconver- sionfromenergyneedtodeliveredenergy.Archetypespecificand timedependentassumptionsonenergymixanduseoflocalenergy sourcesaremadeuseofintheanalysis.
Table2
Overviewofparametervaluesin2016and2050,withdatasourcesandshortexplanations.
2016value Source/comment 2050value Source/comment
Population 5.2million StatisticsNorway[42] 6.7million StatisticsNorway[42]
Personsperdwelling 2.2 StatisticsNorway[43] 2.1 Assumption
Averagelifetimeofdwellings 125years EstimationinlinewithBohne etal.[44]
125years Assumedcontinuationoftrends Averageheatedfloorareaperdwelling Segmentspecific StatisticsNorway[45] Segmentspecific Assumedcontinuationoftrends Renovationcycle(deeprenovationof
facades)
40years Estimationinlinewith Kristjansdottiretal.[46]
Scenariospecific Assumedcontinuationoftrends Energyneedintensitiesforheatingand
domestichotwater
Archetypespecific EPISCOPE[37] Archetypeandscenario specific
EPISCOPE[37]
Energymix Segmentspecific StatisticsNorway[47] Segmentspecific Assumedcontinuationoftrends
andoutphasingoffueloil Systemefficiencies Energycarrierspecific StandardsNorway[48] Energycarrierspecific Assumedcontinuationoftrends Electricload 4500kWh/dwelling StandardsNorway[48] 4500kWh/dwelling Assumedcontinuationoftrends Sharehavingheatpump Segmentspecific StatisticsNorway[49] Scenariospecific Continuationof
trends/assumptions
AverageCOP Segmentspecific StatisticsNorway/Standards
Norway[47,48]
Scenariospecific Assumptions Shareofheatingdemandcoveredby
heatpump
40% PrognosesenteretAS[50] Scenariospecific Assumptions
Sharehavingphotovoltaics 0 StatisticsNorway[49] Cohortandscenario
specific
Assumptions Energyproductionfromphotovoltaics Segmentspecific EPISCOPE[37] Segmentspecific EPISCOPE[37]
Outdoorclimate(HDDfactor,relative differenceinheatingneedfrom 1961–1990average)
0.88 NorwegianMeteorological
Institute[51–53]
0.76 NorwegianMeteorological
Institute[51–53]
Averagethermaladaptationfactor (Real/theoreticalenergydemand)
1.37 TrendlinepresentedinFig.2 0.91 TrendlinepresentedinFig.2
Table3
Cohortdefinitionandaverageheatedfloorareapersegment.
Cohortnumber 0 1 2 3 4 5 6 7
Startyear – 1801 1956 1971 1981 1991 2001 2011 Endyear 1800 1955 1970 1980 1990 2000 2010 2020 HFASFH(m2) 133 133 139 144 161 139 142 152
HFATH(m2) 88 88 101 100 96 85 88 96
HFAMFH(m2) 56 56 53 61 64 58 60 68
Norwayisauniquecaseinternationally,astheenergymixfor dwellingsisbyfardominatedbyelectricity[21].Thecurrentoverall energymixforheatinganddomestichotwateris77%electricity, 19%bio-fuelsandwood,3%fueloiland2%districtheating.Fuel oilisphasedoutby2020[55].Onlyheatpumpsandphotovoltaics (PV)areconsideredrelevantforutilizationoflocalenergysources intheNorwegiandwellingstocksystem.
Theenergydemandforspaceheatingiscorrectedforexpected futuredevelopmentinoutdoorclimate.TheofficialNorwegianpro- jectionsfor thetemperaturechangeaccording toIPCC’sRCP4.5 scenarioisapplied[52,53].
Athermaladaptationfactorisdevelopedandappliedtocorrect forallfactorsthatmaketherealenergydemandforheatingand hotwaterdifferfromthetheoreticalestimate.Userbehaviourwill dominatethefactor,andtheadaptationfactorcorrectsforheating habitsvaryingbetweendwellingsofdifferentenergystate.Empir- icaldatafromvarioussources[47,56,57]areusedtoestimatethe averagedivergenceofrealenergydemandfromthetheoreticalesti- mateindwellingsofdifferenttypeandenergystate.InFig.2,the 45◦ lineindicatesthesituationwherethereisnoadaptation,as therealenergydemandisequaltothetheoreticalestimate.The lineartrendlineresultingfromtheempiricaldata,however,isless steepthanthe45◦ line.Thisimpliesthatinveryenergyefficient buildings,theaveragerealenergyuseishigherthanthetechnical estimate,andinveryinefficientbuildings,theaveragerealenergy useislower.Accordingtotheavailableobservations,theturning pointisindwellingswithtechnicalestimatedenergydemandof
about100kWh/m2,wheretheaveragemeasuredrealdemandis thesame.
The dwelling stock energy model is calibrated against real historical development in our previous publications [33,36,38].
Themodelresultsarecomparedwithstatisticsonrealdwelling stockdevelopment[36,38],construction,demolitionandrenova- tionactivity[38]andcalibratedagainstenergystatistics[33].The modelwasfoundsuitableforthistypeofanalyses.Naturally,itis notpossibletocalibratethefuturedevelopmentastherealdevel- opmentisnotyetknown.
2.3. Scenarioanalysis
Ascenarioanalysisiscarriedouttoevaluatetheeffectsofdif- ferentpossiblestrategiesforenergysavingsinthedwellingstock system.Theconceptualoutlineofthescenarioanalysisispresented inFig.3andthedetailedspecificationsofallscenariosarelisted inTable4.TheBaselinescenarioisconstitutedbytheassumptions onfuture developmentinthesystemthat areconsideredmost likely,accordingtothepresentcommonpractice,recenttrends andknownpolicies,regulationsforthenearfutureandqualified assumptions.Threealternativedevelopmentpathsdifferfromthe baseline:1)Advancedratherthanstandardrenovation,2)rapid introductionof localenergysources and 3)morefrequent ren- ovation. Theeffect of each strategy as wellascombinations of themareevaluatedinScenario1–6.Thecombinationofadvanced andfrequentrenovationconstitutesthe‘Minimizingenergyneed scenario’,whereas thecombination of all three constitutes the
‘Minimizingdeliveredenergyscenario’.Theestimatedtotaldeliv- eredenergywillbethefinal modeloutcomeofallscenarios,in GWh/year.
2.4. Uncertaintyofinputparameters
In a perturbation analysis, various inputs are changed marginally,oneatatime,andtheeffectsonthefinalresultsare studied[58].Thiseffectismeasuredthroughthesensitivityratio
Fig.2.Thermaladaptationfactortrendline.Lineartrendlinefromempiricalobservationsformeasuredversuscalculatedenergyuse(currenttrends).
Fig.3. Conceptualoutlineofthescenarioanalysis.Thelinesbetweenthescenariosindicatehowthescenariosbuildoneachother,astheyeitherincludeachangefromthe Baselinescenario(Scenario1–3),orcombinationsofchanges(4–6).
Table4
Scenariospecification.
Scenariodescription Ambitionlevelof
renovationafter2020
Useoflocalenergysources Renovationcycle after2020
0.Baseline Standardrenovation Followingtrends/expectedfuturedevelopment 40years
1.Advancedrenovation Advancedrenovation Followingtrends/expectedfuturedevelopment 40years 2.Extensiveuseoflocalenergysources(heat
pumpsandphotovoltaics)
Standardrenovation ExtensiveuseofHPandPV 40years
3.Morefrequentrenovation Standardrenovation Followingtrends/expectedfuturedevelopment 30years 4.Advancedrenovationandextensiveuseof
localenergysources
Advancedrenovation ExtensiveuseofHPandPV 40years
5.Minimizingenergyneed:Advancedand frequentrenovation
Advancedrenovation Followingtrends/expectedfuturedevelopment 30years 6.Minimizingdeliveredenergy:Advancedand
frequentrenovationandextensiveuseof localenergysources
Advancedrenovation ExtensiveuseofHPandPV 30years
(SR)whichisthefractionoftherelativechangeintheresult(R/R0) overtherelativechangeintheinputparameter(P/P0),asshown inEq.(1)[59],whereP0istheinitialparametervalueandR0isthe initialresult
SR==R/R0
P/P0
(1)
Inthisstudy,thesensitivityratioisusedtoquantifytheeffect ofachangeinselectedinputparameters,onthefinalresults,for deliveredenergyintheendyearoftheanalysis,2050.
Thereisuncertaintyrelatedtoallmodelinputparameters.How- ever,theuncertaintyofstock-relatedinputsisstudiedindetailin previouspublications[33,60]andwillnotberepeatedhere,astheir findingsarevalidalsoforthepresentapplicationofthemodel.The uncertaintyoftheenergy-relatedinputparameterswillhowever beexplored,asthisisnotcoveredinearlierpublications.Table5 givesanoverviewofthesevariables,theirsources,anevaluationof theiruncertaintyandhowtheyarevariedintheperturbationanal- ysis(lowandhighvariants).Formostvariables,a±10%changeof the2050valueisusedintheperturbationanalysis.Fortheaver- agesystemefficiency,a+10%changewouldresultinanefficiency
largerthan1,whichisimpossible.Therefore,±2%isusedforthis variable.Fortheoutdoorclimate±5%isused,asalargerdecrease wouldleadtotemperatureslowerthanthecurrentones,whichis notrealistic.
3. Resultsanddiscussion 3.1. Evolutionofthedwellingstock
Theevolution oftheNorwegiandwellingstockfrom1960to 2050ispresentedinFigs.4and5.Bothfiguresshowhowthetotal stockofheatedfloorareahasmorethandoubledfrom115million m2in1960tothecurrentlevelof255millionm2,andthatthestock isexpectedtoincreasefurtherto324millionm2by2050.Thehis- toricalstockgrowthisduetoincreaseinthepopulationcombined withadecreaseinthenumberofpersonsperdwelling.Thefuture stockgrowthismainlydrivenbytheexpectedfurtherincreasein thepopulation,asthenumberofpersonsperdwellingisexpected tostabilize.Thetotalstocksizeandcompositionofsegmentsisthe sameinallscenarios,butthefutureenergystateofdwellingsinthe varioussegmentsvariesbetweenthescenarios.
Thedynamicsofthestock(dwellingstockmodeloutputs)are presentedinFig.4asthechangingstockcompositionofheated floorareaindwellingsofvariousrenovationstatesandconstruc- tionperiods,accordingtotheBaselinescenario.Theareamarked A inFig. 4 representsdwellingsconstructed prior to2020 and remainingintheiroriginalstateorrenovatedbefore1980(without a significantenergy-saving effect). Area Brepresentspast con- structionsthat has beensubject tohistorical renovation in the period1980–2020(withanenergy-savingeffectcorrespondingto asegment-specificstandardrenovation).AreaCrepresentscon- structions subject to future renovation after 2020, and area D representsfuturenewconstructionafter2020(accordingtopas- sivehouseenergystandards).Hence,AandBrepresenttheshare ofthestockthatisnotexpectedtobechangedwithrespectto energystatein theperiod2020–2050,iffollowing the‘natural’
renovationcycle.Theactualpotentialforimprovedenergyeffi- ciencyin thestockis hencelimited toCand D.Therefore, 50%
ofthe2020stockisexpectedtobeunchangeduntil2050,either stillremainingintheoriginalstateorinthestateofahistorical renovation.Toimprovetheenergyefficiencyofthestock,itisthere- forehighlyimportantthattheopportunityistakentointroduce energy-efficiencymeasureswhen dwellingsare renovatedafter 2020(C),andthatnewconstruction(D)areasenergy-efficientas possible.Ifreducingtherenovationcycleto30yearsfrom2020 onwards, as in Scenario 3, a larger share of the stock will be renovated,but36%ofthe2020stockwillstill beunchangedby 2050.
Fig.5showshowtheenergyefficiencyofthestockevolvesover time,accordingtotheBaselinescenario.Animportantimprovement intheenergyefficiencyofthestockhasalreadytakenplaceand isexpectedtocontinuetowards2050.Thegreywedgebandrep- resentsoldbuildingsofpoorenergyefficiency,withenergyneed intensitieshigherthan150kWh/m2.After1980,theyaredecreas- inginnumberastheyareeitherupgradedthroughrenovationor phasedoutthroughdemolition.Thesolidyellowwedgebandisthe shareofthestockbeinginitsoriginalstatewithanenergyneed intensityintherange101–150kWh/m2,whereas thepatterned yellowis thesharethat hasreachedthis levelthroughrenova- tion.Thecorrespondinggreenandbluewedgebandsrepresentthe higherenergystandardrangesof51–100kWh/m2andlargerthan 51kWh/m2,respectively.
Fig.5isaneffectivewayofvisualizingthesharesofthedwelling stockthatrepresentvariousenergy-efficiencylevels,foranyyear between1960and2050.Asanexample,in1960alldwellingsper-
formedworsethan150kWh/m2,andin2000roughlyhalfofthe dwellingsperformed inthe range 101–150kWh/m2. The figure showsthat,accordingtotheBaselinescenario,after2020onlyavery smallshareofthedwellingstockwillhaveanenergyneedinten- sitylargerthan150kWh/m2.Further,alargeshareofthefuture stockwillbeintherangeof101–150kWh/m2,theshareofthe stockbeingintherange51–100kWh/m2willberatherstable,and thesharehavinganenergyneedintensitylessthan51kWh/m2is increasingsteadily.
Iftheenergyefficiencyafterrenovationorthefrequencyofthe renovationdiffersfromtheassumptionsintheBaselinescenario,the wedgebandsinFig.5wouldevolvedifferently.InTable6,snapshots ofthestockcompositionin2016,2030and2050arepresentedfor thescenariosthatinvolvedifferentrenovationregimes(Scenarios 1,3and5).Graphsshowingthefulltimeseriesforthesescenarios aregiveninAppendixD.
Table6demonstratesthattheshareofveryinefficientdwellings withenergyneedintensitieshigherthan150kWh/m2isdecreas- ingtothesamelevelregardlessofthechoiceofrenovationregime.
Evenwithoutadvancedormorefrequentrenovation,standardren- ovationof‘normal’cyclesisexpectedtoreducethissharetothe levelof thedwellingsthatremainin theoriginalstatefor her- itagereasons.Furthermore,thefutureshareofveryenergyefficient dwellings doesnot differ much between the scenarios. This is becauseonlycohort7and8canreachanenergyneedintensity below51kWh/m2.Incohort7,thisisaneffectfromeitherstan- dardoradvancedrenovation,andincohort8eveninoriginalstate.
Hence,onlyamorefrequentrenovationofcohort7(Scenario3and 5)willincreasetheshareofveryenergyefficientdwellings.
However, there are significant differences betweenthe sce- narios regarding future shares of the stock in the range of 51–100kWh/m2ratherthan101–150kWh/m2.Advancedrenova- tion(Scenario 1and 5) willstrongly increase this share, while the share being in the range of 101–150kWh/m2 will remain unchangedifmorefrequentstandardrenovationisapplied(Sce- nario3).
Whenmodellingdevelopmentsindwellingstockenergyuse, underlyingmodelsandassumptionsareusedtodescribehowthe dwellingstockandenergyintensitiesarechangingovertime.Real- isticmodelsareneededtoavoidthatasimplifiedunderlyingstock modeland/orrenovationratesleadstounrealisticresultsforthe estimatedfutureenergydemand.Thepresentedsegmentedand dynamic dwellingstock modelgives a detailed descriptionand thoroughunderstandingofthechangingsizeandcompositionof dwellingstocks,intermsofdwellingtypes,cohortsandrenova- tionstate.Themodelthereforeformsanecessarybasisandiswell suitedtobeappliedforenergyanalyses.
3.2. Scenarioresultsontotaldeliveredenergy
The scenario results on total delivered energy are given in Figs.6and7.Fig.6showsthetotaldeliveredenergyforallscenarios inyears2016,2030and2050,bothasthetheoreticaltechnicalesti- mateandtheestimated‘real’deliveredenergyafterapplyingthe thermaladaptationfactorandaccountingforoccupantbehaviour.
Thetechnicalestimateddeliveredenergyisdecreasinginallsce- narios.TheBaselinescenarioshowsareductionof23%from2016 to2050,whichisasignificantimprovementgiventhe27%stock growthduringthesameperiod.Onlyminorfurtherreductionsare expectedinScenario1,3and5,whichassumemoreuseofadvanced and/orfrequentrenovationthan theBaselinescenario.Thisisin starkcontrasttothesignificantreductionsshownforScenario2, 4and6,whichinvolvemoreextensiveuseoflocalenergysources byuseofheatpumpsandPVthanintheBaselinescenario.Thisisa veryinterestingfinding;extensiveuseoflocalenergysourceshas
Table5
Variablestobestudiedinthesensitivityanalysis.
Parameter Source Evaluationofthedata
uncertainty
Variationsappliedinthe perturbationanalysis
Energyneedintensities TABULA High ±10%in2050
Contributionfromheatpump NS3031andassumptions High ±10%in2050
Contributionfromphotovoltaics TABULA Medium ±10%in2050
Averagesystemefficiency Assumedcontinuationoftrends Low ±2%in2050
Futureoutdoorclimate(HDD) Officialprojections High ±5%in2050
Electricload Assumedcontinuationoftrends High ±10%in2050
Adaptationfactor Statisticsandcasestudies High ±10%in2050
Fig.4.Dwellingstock(heatedfloorarea)evolution1960–2050.Sharesofthestockbeinginoriginalstate,renovated1980–2020,renovatedafter2020andconstructedafter 2020.Baselinescenario.
Fig.5.Sharesofthestockbeingofdifferentstatesandenergyneedintensities.Evolution1960–2050.Baselinescenario.
thepotentialformuchlargerreductionsintotaldeliveredenergy thanimplementationofadvancedandmorefrequentrenovation.
Scenario6,which is themostoptimistic scenario,showsa 52%
reductionintheoreticalestimatedtotaldeliveredenergyfrom2016 to2050.
Thelimitedeffectofambitiousandmorefrequentrenovation isexplainedbytheappliedarchetype-specificenergyneedinten- sities(presentedinAppendixB).Evenstandardrenovationofold
dwellingsconstructedbefore1955givesanenergysavingofupto 100kWh/m2.Indwellingsconstructedintheperiod1956–2000, savingsupto80kWh/m2arepossible,andthepotentialforsav- ingsthroughstandard renovationofdwellingsconstructedafter 2000isupto30kWh/m2.Largesavingsarepossiblethroughstan- dardrenovationofexistingdwellingsintheiroriginalstate,buta majorshareofthispotentialhasalreadybeenrealizedinthepast.
Theremainingpotentialforenergyefficiencythroughrenovationis
Table6
Sharesofthestockhavingvariousenergyneedintensitiesin2016,2040and2050,accordingtotheBaselinescenarioandScenario1,3and5.
<51kWh/m2 51–100kWh/m2 101–150kWh/m2 >150kWh/m2 Total
millionm2 Shares millionm2 Shares millionm2 Shares millionm2 Shares millionm2 Shares
2016 Allscenarios 0 0% 53 21% 151 59% 50 20% 255 100%
2030 Baseline 38 13% 68 24% 162 56% 19 7% 288 100%
Scenario1 38 13% 97 34% 133 46% 19 7% 288 100%
Scenario3 38 13% 67 23% 165 57% 17 6% 288 100%
Scenario5 38 13% 105 36% 127 44% 17 6% 288 100%
2050 Baseline 118 36% 58 18% 136 42% 12 4% 324 100%
Scenario1 118 36% 118 36% 76 24% 12 4% 324 100%
Scenario3 133 41% 43 13% 136 42% 12 4% 324 100%
Scenario5 133 41% 119 37% 60 18% 12 4% 324 100%
thereforelimitedtostandardrenovationofthesmallshareofolder existingdwellingsthatarestillintheoriginalstate,standardren- ovationofnewerexistingdwellingswherethesavingsarelower, andfurtherupgradesoftheexistingstockthroughadvancedren- ovation.Advancedrenovationcangiveadditionalsavingsofupto 84kWh/m2,comparedtoastandardrenovation,andthepotential savingsarelargestinolderdwellings.
Intotal,itisobviousthatimportantworkhasalreadybeencar- riedouttoimprovetheenergyefficiencyofthedwellingstock.
Itisstillpossibletoachievefurthersavingsthroughrenovation, butalargeshareofthepotentialhasalreadybeenused.Newcon- structionfromafter2020isassumedtoalwaysbeofpassivehouse standard,whereadditionalreductionsintheenergyneedintensity islikelynotpossiblethroughrenovation.Savingsinthedelivered energytotheseareonlypossiblethroughincreaseduseoflocal energysources.
Fig.6showsthatwhenassumingextensiveuseoflocalenergy sources,thereisasubstantialfurtherdecreaseinthetechnicalesti- matedtotaldeliveredenergyin2050.InScenario6(theMinimizing deliveredenergyscenario)whereextensiveuseofheatpumpand PViscombinedwithfrequentandadvancedrenovation,thetech- nicalestimateddeliveredenergyis52%lowerin2050thanin2016, despitethe27%growthintotaldwellingstock.
In2016,theestimated‘real’deliveredenergy,afterapplyingthe thermaladaptationfactor,is7%lowerthanthetechnicalestimated deliveredenergy.Hence,thereisanaggregated preboundeffect inthedwellingstockin2016,wheretheuserbehaviourincluding heatinghabitsonaverageleadstolowerestimated‘real’energy usethanthetechnicalestimate.ThiscanbeexplainedbyFig.5 thatshows howthe2016stockis still dominatedbydwellings withaverage energy need intensities larger than 100kWh/m2. However,by2030,thenumberofveryinefficientdwellingshas decreased and a substantial number of dwellings with energy needintensitybelow100kWh haveentered thestock.By then, anaggregatedreboundeffectoccursinallscenarios,asthecom- fortfactorisexpectedtomake therealenergyusehigherthan thetechnicalestimate,accordingtowhat is observedin highly energy-efficientbuildingstoday(seeFig.2).By2050,thisrebound effectissurprisinglylarge,astheestimatedtotal‘real’delivered energy is 20–28%largerthan thetechnical estimatein all sce- narios.IntheBaselinescenario,theestimatedtotal‘real’delivered energyonlydecreasesonlyby1%from2016to2050.Hence,user heatinghabitsindwellingswithhighenergystandardmaycoun- teract a significant improvement in real total delivered energy in future. This is a highly policy-relevant and important find- ing. The expected future rebound effect should be addressed furtherinaggregatedstudiestoobtaindeeperinsightonthisphe- nomenon.
Furthermore,thehistoricalandexpectednear-futureelimina- tionofthepreboundeffectisdesiredfromauserperspective,as
thisisobtainedbyraisingtheaverageindoortemperaturetoacom- fortablelevel.However,furtherincreaseintheindoortemperature, recognizedasthecomforteffectinveryenergy-efficientbuildings, isunnecessaryandshouldbeavoided.Policyshouldthereforeaim atcounteractingthis futurelikelylargereboundeffectbyinflu- encinguserbehaviourthroughinformation,smartsteeringofthe energyuseandsmarthousedesignprinciples,aswellastheintro- ductionofdifferentiatedenergypricestructures(suchastheuseof two-steptariffs).
Regardlessoftheeffectsofapplyingtheadaptationfactor,the simulations showthat use oflocal energy sources hasa much largerpotentialforfuturereductionsintotaldeliveredenergythan ambitiousormorefrequentrenovation.Thisisanotherinterest- ingfinding,asuseoflocalenergysourcesisalsoconsideredmore cost-efficient.
Fig.7showstheenergymix,aswellastheenergyfromheat pumpandPV,in2016andinthevariousscenariosin2050.The deliveredenergyforheatingandhotwaterisexpectedtodecrease inallscenarios,by15–65%.Again,thelargestreductionsarefound inthescenarioswithextensiveuseoflocalenergysources.
Fig.7 also demonstrates how theelectric loadwill increase inimportance,bothinabsoluteandrelativeterms.Thetotalel- specificenergyuseisexpectedtoincreaseby34%from2016to 2050.Further,theshareofthetotaldeliveredenergybeingelectric loadisexpectedtoincreasefrom28%in2016to37–59%by2050in thevariousscenarios.Finally,from2016to2050,thelocalenergy utilizedbyheatpumpsandPVisexpectedtoincreaseby90–130%
inthescenariosassumingthebaselineuseoflocalenergysources andby325–385%inthescenariosassumingextensiveuseofthese.
However,thecontributionfromPVwillnotbeabletocoverthe electricloadin2050,notevenwhenassumingextensiveuseofPV.
Furthermore,themodelcanbeusedtostudytheimportanceof variousstocksegmentsontheresultsforthetotalstock.Although theincreaseofthedwellingstockwillmainlybeinterracedhouses andmultifamilyhouses(THsandMFHs),theenergyuseinthesys- temwillstillbedominatedbyoldsingle-familyhouses(SFHs)in 2050.SFHsalsohavethelargestpotentialforproductionofelec- tricityfromPV.SegmentedresultsaregiveninAppendixD.
Finally, the presented future scenario resultsshould not be regardedaspredictionsthataimatprojectingpreciselythefuture developmentinenergydemand.Theexactvalueofthesimulated energydemandineachofthescenariosine.g.2050isoflessimpor- tancethaninsightstohelpunderstandingtheexpecteddifferences betweenpotentialfuturedevelopmentpaths,andidentifyingthe maincausesofthesedifferences.Thevariousfactorsintheseg- menteddynamicdwellingstockmodelaredefinedpreciselyand varybetweenthescenarios,andhencethemodelcanbeusedto evaluatetheimportanceofeachfactor.
0 5000 10000 15000 20000 25000 30000 35000 40000 45000
Technical esmated delivered energy
Esmated "real"
delivered energy
Technical esmated delivered energy
Esmated "real"
delivered energy
Technical esmated delivered energy
Esmated "real"
delivered energy
2016 0 2030 0 2050 0
GWh
Baseline S1: Advanced renovaon
S2: Extensive HP + PV S3: Frequent renovaon
S4: Advanced renovaon and extensive HP+PV S5: Advanced and frequent renovaon S6: Minimizing delivered energy
Fig.6. Scenarioresultstotaldeliveredenergyin2016,2030and2050.‘Technicalestimateddeliveredenergy’referstotheenergydemandcalculatedaccordingtothe technicalstandardofthedwellingswhile‘Estimated“real”deliveredenergy’istheestimatedenergydemandaftercorrectingforprebound/reboundeffect.
Fig.7.Energymixin2016andinallscenariosin2050.Estimated‘real’totaldeliveredenergy.Thenetthermaldeliveredenergyequalsthesumofelectricityforheatingand dhw,bio,fueloilanddistrictheating.Thetotalnetdeliveredenergyalsoincludeselectricload.Thelocalenergyusedineachscenarioisthesumof‘Heatpumpcontribution’
and‘Photovoltaicscontribution’.
3.3. Sensitivityanalysis
Theperturbationanalysisisusedtoevaluatetheeffectofanarbi- trarychangeintheenergy-relatedinputparametersonthemodel resultsfortotaldeliveredenergyin2050.Theinputparametersare changedoneatatime,asdescribedinTable5.
Theabsolutevalueof thesensitivity ratioSRinyear2050is showninFig.8forboththeBaselinescenarioandScenario6Min- imizingdelivered energy.For someof theinput parameters,the corresponding sensitivity ratio is negative, which tells that an increaseintheinputleadstoadecreaseintheresultsandviceversa.
Thisisthecaseforthecontributionfromheatpump,contribution fromPVandaveragesystemefficiencies.Theabsolutevaluesofthe correspondingSRsarepresentedinFig.8forbetterreadability.
Theeffectofchangingtheinputparametersisdifferentinthe BaselinescenarioandtheMinimizingdeliveredenergyscenario.The highestSRvalueis0.63,andisfoundfortheadaptationfactorinthe
Baselinescenario.InScenario6,thecorrespondingSRis0.41.This isbecausetheenergyforheatinganddhw,whichtheadaptation factorisappliedto,hasalargershareofthetotalenergydemand intheBaselinescenario.Changesinthefutureoutdoorclimatealso haveahigherrelatedSRintheBaselinescenariowherethereisa higherenergyneedforheating.However,this parametershows lowSRvaluesthatindicateamuchlowereffectontheresult.
Onthecontrary,thecontributionfromheatpump,contribution fromPVandtheelectricloadhavehighersharesinScenario6and therelatedSRsarehigherthanintheBaselinescenario.Theeffectof changesintheenergyneedintensitiesandtheaveragesystemeffi- cienciesisalsohigherinScenario6thanintheBaselinescenario.This isbecausetheparameterchangeleadstoacorrespondingchange inthesumofenergydeliveredfromcarriersandPV.However,the energyfromthePVisnotaffectedbythechangesintheparame- ters.Hence,therelativechangeintheenergysuppliedbycarriers
islargerinScenario6wherethePVhasalargershareofthetotal energydemand.
Intotal,theabsolutevalueofSRisneverhigherthan0.63andin mostcasessmallerthan0.50,meaningthatagivenchangeininput parameters,say15%increase,giveslessthan7.5%changeintotal deliveredenergy.Accordingtothis,weregardtheoverallmodel andscenarioanalysistorepresentanacceptablelowuncertainty inhowscenarioresultsareestimated.Theresultsinscenario6are moresensitivetochangesininputparametersexceptforchanges inadaptationfactorandoutdoorclimate.IntheBaselinescenario, theadaptationfactoristheinputparameterofhighestsensitivity onthefinalresults,andtheimportanceofuserbehaviourshould againbestressed.
4. Conclusions
Thepresentedsegmenteddynamicdwellingstockmodelpro- videsanewapproachfordwellingstockenergyanalyses.Itisthe firstmass-balanceconsistentstock-drivenmodelthatisapplied forenergyanalysesofdwellingstocks,withmodelinternalestima- tionofrenovationactivity.Itprovidesadetailedunderstandingof thelong-termevolutionofdwellingstocksandthesystemdynam- ics.Suchamodelapproachisnecessarytounderstandthishighly dynamicsystemandtogivethelevelofdetailthatisrequiredto giverealisticresultsfortheanalysisoffuturedwellingstockenergy demand.
Whenapplied for energy analyses of the future Norwegian dwellingstock,themodelhasuncoveredseveralimportantcause- effectrelationships.Ithasproventobeapowerfultoolasitgives informativeresultsinthescenarioanalysis.Thesimulationsshow thatthetotalheatedfloorareainthedwellingstockisexpected tokeepgrowingby27%towards2050,mainlyduetopopulation growth.However,onlyashareofthestockisatargetforenergy- efficiencyimprovementsofthebuildingenvelopesintheperiod 2020-50.Thissharerepresentnewconstructionordwellingsthat arelikely toberenovatedinthisperiod.Asmuchas50%ofthe 2020stockwillbeunchangedtowards2050,asthesedwellings willnothavea‘natural’needforrenovationordemolitionduring thisperiod.
Still,animportantfurtherimprovementoftheoverallenergy- efficiencystateof thestock is expected,through renovation or demolitionofold inefficientdwellingsandnewconstruction of passive-housestandard.Thisexpectedimprovementofthestock willleadtooverallenergysavingsinthestockeveninourBaseline scenario,whichincludestheleastambitiousassumptionsinour study.
Thescenarioanalysisshowsthatfurtherenergysavings,beyond theBaselinescenario,arepossibleifincludingadditionalmeasures.
More ambitious or more frequent renovation, commonly men- tionedasimportantwaystoobtainenergysavings,werefoundto haveonlyalimitedeffectontheoverallenergysavingstowards 2050.Extensiveuseoflocalenergysourcescangivemuchhigher energysavingsinthesystem.Ifassumingextensiveuseofheat pumpandphotovoltaics,thetechnicalestimateddeliveredenergy forheatingandhotwaterin2050willbeabout50%lowerthanin theBaselinescenario.Ifassumingextensiveuseofheatpumpand photovoltaics,inadditiontomorefrequentandadvancedrenova- tion,thetechnicalestimateddeliveredenergyforheatingandhot waterin2050is65%lowerthanintheBaselinescenarioand80%
lowerthanthecorrespondingestimatefor2016.
Theelectricloadwillbeoflargerimportanceinthesystemin future.Thetotalelectricloadwillincreaseby34%from2016to 2050.Furthermore,theshareofthetotaldeliveredenergybeing electricloadisexpectedtoincreasefrom28%in2016to37–57%in thevariousscenariosin2050.
Fig.8.Theabsolutevaluesofthesensitivityratiosin2050fortheBaselinescenario andScenario6.
Theestimated‘real’reductionsinenergydemand,afterapply- ingtheadaptationfactorand correctingfor userbehaviour,are significantlylowerthanthetechnicalestimate.In2016,thereis asituationwithanaggregatedpreboundeffect.Theoverallenergy efficiencyofthestockisstillsolowthattherealuse−onaverage
−islowerthanthetechnicalestimate.Thisisexpectedtochange innearfuture. By2030,and even moreevident by2050,there willbeasignificantaggregatedreboundeffectifcurrentobserva- tionsforrealenergyuseinhighlyenergy-efficientbuildingsstill hold.Hence,userbehaviourmightpreventthetechnicalestimated energysavingpotentialinthesystemsfrombeingrealized.
Thereishighuncertaintyrelatedtomanyoftheinputparame- tersinthemodel.Aperturbationanalysisiscarriedouttoanalyse thesensitivityof theenergy-relatedinputs onthefinal results.
Noinputparameterswithhighuncertainty werefoundtohave a strong influence on the model output result, and therefore weconcludethattheoverallmodelandscenarioanalysisrepre- sent anacceptablelow uncertainty in how scenarioresultsare estimated.
Ourhypothesispresentedintheintroductionofthispaperwas thatbyintroducingcurrentlyavailabletechnologyinrefurbished andnewbuildings,itwillbepossibletoreducetheenergydemand intheNorwegiandwellingstockbysome50%by2050,despite strongstockgrowth.Theresultsshowthatthehypothesismay beconfirmedforthetheoreticalestimatedtotaldeliveredenergy inourmostoptimisticscenario.However,inthis scenario,user behaviouris expected toreduce thesavingpotential from51%
forthetheoreticalestimateto36%fortheestimated‘real’energy demandfrom2016to2050.Thepolicyimplicationsofthisisthat effortsshouldbemadetocounteractthisreboundeffect.Unless suchmeasuresaretaken,thehypothesiswillnotholdandpolicy targetsmightnotbemet.
Acknowledgements
This paper is published as a result of participation in the EPISCOPEresearchproject(EnergyPerformanceIndicatorTrack- ingSchemes fortheContinuousOptimisationof Refurbishment Processes in European Housing Stocks), with co-funding from the ‘Intelligent Energy − Europe’ Programme, contract No.
IEE/12/695/SI2.644739).
