Building model calibration using energy and environmental data

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ContentslistsavailableatScienceDirect

Energy

and

Buildings

jo u r n al h om ep a g e :w w w . e l s e v i e r . c o m / l o c a t e / e n b u i l d

Building

model

calibration

using

energy

and

environmental

data

Mohammad

Royapoor

∗

,

Tony

Roskilly

SirJosephSwanCentreforEnergyResearch,StephensonBuilding,NewcastleUniversity,NewcastleuponTyneNE17RU,UnitedKingdom

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received1December2014

Receivedinrevisedform18February2015 Accepted21February2015

Availableonline28February2015

Keywords:

Modelcalibration Measuredenergydata Localweatherdata

Buildingperformancesimulation EnergyPlus

Hourlydata Sensordeployment Casestudybuilding

a

b

s

t

r

a

c

t

Alargenumberofrandomlyinteractingvariablescombinetodictatetheenergyperformanceofabuilding.

Buildingenergysimulationmodelsattempttocapturetheseperturbationsasaccuratelyaspossible.

Thepredictionaccuracyofbuildingenergymodelscannowbebetterexaminedgiventhewidespread

availabilityofenvironmentalandenergymonitoringequipmentandreduceddatastoragecosts.Inthis

paperasetoftwocalibratedenvironmentalsensorstogetherwithaweatherstationaredeployedina

5-storeyofﬁcebuildingtoexaminetheaccuracyofanEnergyPlusvirtualbuildingmodel.UsingAmerican

SocietyofHeating,RefrigeratingandAir-ConditioningEngineers(ASHRAE)Guide14indicesthemodel

wascalibratedtoachieveMeanBiasError(MBE)valueswithin±5%andCumulativeVariationofRoot

MeanSquareError(CV(RMSE))valuesbelow10%.ThecalibratedEnergyPlusmodelwasabletopredict

annualhourlyspaceairtemperatureswithanaccuracyof±1.5◦_C_for_99.5%_and_an_accuracy_of_±₁◦_C_for

93.2%ofthetime.

(http://creativecommons.org/licenses/by/4.0/).

1. Introduction

Theoriginsofbuildingenergymodellingcanbetracedbackas earlyas1920swiththedevelopmentofresponsefactormethod fortransientheatflowcalculations[1].Theavailabilityof comput-ersinthe60sheraldedanewdawnwhen,especiallyfromearly 80stheHVAC companiesdeveloped energy modelsfor heating andcoolingloadcalculations[2,3].Thistrendacceleratedasthe 70soilcrisesraisedbuildingenergystandards,leadingtogreater energy efficiency and modelling methods that continue to this day[4].Whilstinitiallyadesignphasetool,increasinglybuilding energysimulation(BES)modelsofexistingbuildingsaredeveloped toaidresearchintomodel-based controls, optimisation,energy conservationmeasures(ECM)andfinancialappraisals[5–7].The creation, maintenance and updating of virtual buildingmodels thereforeincreasinglyrequiregreaterlevelsofaccuracytoenable moremeaningfulstudies.

Abbreviations: AHU,airhandlingunits;BES,buildingenergysimulation;BIM,

buildinginformationmodelling;BWM,Boxwhiskermean;CV(RMSE),cumulative variationofrootmeansquareerror;ECM,energyconservationmeasures;EPW, EnergyPlusweather(ﬁle);HAM,heat,airandmoisture(Modelling);HVAC, heat-ing,ventilation,air-conditioningandcooling;MBE,meanbiaserror;PIR,passive infra-red.

∗Correspondingauthor.Tel.:+4401912085869.

E-mailaddresses:[email protected],[email protected]

(M.Royapoor).

Inthelastfewyearsthewidespreaddeploymentof multifunc-tionalenvironmentalsensors,mandatorysub-meteringofbuilding energyconsumptions,longitudinaldatacollectionandthe inter-netofthingshaveallledtosubstantialamountsofbuildingand energy related databeingmadeavailable. Therichness of digi-talinfrastructure outputhasgrowntoanextentcomparable to biologicalecosystemsinalltheircomplexity[8].Withinbuilding relatedapplications,theavailabilityofbothsimulatedand mea-suredenergyandcomfortdatagivestheissueofmodelcalibration greaterpotency.Buildingmodelcalibrationisameasureofmodel accuracy,whichdespiteincreasingsophisticationstillsuffersvast under-determinedparameterspace[9].

Theaimofthispaperistoconductanenergycalibrationusing anEnergyPlusmodelbeforeexaminingthematchbetween simu-latedandactualspaceairtemperatures.Thistwo-tieredobjective isperusedthroughthefollowingsteps:

1Usinganofﬁcebuildingasaplatform,aﬁrststageenergy cal-ibrationofthemodelisperformedusing2012hourly-metered values.

2Spaceairtemperatureiscollectedforthesameperiodusing envi-ronmentalsensorstoenableanassessmentofzonetemperature predictionaccuracyofthecalibratedEnergyPlusmodel.

ThecalibrationprocessfollowsASHRAEguideline 14 recom-mendations.Thisguidelinewasoriginallydevelopedtoquantify energysavingpotentialsofproposedretroﬁtschemes,andisamong

http://dx.doi.org/10.1016/j.enbuild.2015.02.050

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Fig.1.Thebuilding’sheavilyglazedfrontaspectfaces+34◦_from_due_south.

threecurrentguidesthatdeﬁnevirtualmodelacceptancecriteria

[10–12].

1.1. Case-studybuilding

Amodern5-storeysandstoneofﬁceinauguratedin2010isthe platformfortheworkundertakenhere(located54◦58N,1◦36W). Atnorth,east,southandwestorientationssolarcontrolglazing cover54%,29%,87%and 42%ofthefacadesrespectively(hence overallthe building’sexternal facade is53% glazed).The south aspect is partially shaded by extruded aluminium brise soleil (Fig.1).

ThebuildingfabricU-valueexceededthestatutoryUKbuilding requirementsofthetimebyanaverageof29%.Internallythe com-plexarchitectureincludestwolargesoutherlyandwesterlyvoids, twointernalatriafacilitatingdisplacementventilationandablend ofcellularandopenplanofﬁcesover8365m2_of_gross_area

(hous-ingaround500staff).Thermallyinduceddisplacementventilation andexposedconcretesurfacesareamongtwo carbonreduction philosophiesthatguidedtheoriginaldesign(Fig.2).

AGill’sMetPakProweatherstationcombinedwithaSPN1 pyra-nometerweremountedontherooftopofthisbuilding(Appendix A)toprovidethefollowingoutputs(accuracynotedinbrackets): 1Globalsolarradiation(±5%).

2Diffusedradiation(±5%). 3Windspeed(±2%at12m/s). 4Winddirection(±3◦). 5Airtemperature(±0.1◦C).

6Relativehumidity(_±0.8%at23◦C).

7Dewpoint(±0.15◦Cat23◦Cwithadewpointof20◦C). 8Barometricpressure(±0.5hpa).

Except for solar radiation, all measurements are instanta-neousvaluessampledat10-minuteintervals.Globalanddiffused solarcomponents are however10minaverages which are fur-therroundedtoformhourlyﬁgurestoconstructEPW(EnergyPlus weather)ﬁles.

1.2. HVACservices

Threeequallysizedcondensingboilersprovideatotalheating outputof 744kWwhich are deliveredtothe zonesviaa com-binationofradiators, trenchand perimeterheaters, underﬂoor heatingandtemperedair.Twocentralairhandlingunits(AHUs)

of0.96atalltimes.Lightingisoperatedbypresencedetectionand isequippedwithdaylightcompensationsensors.

1.3. Energydata

Box whisker mean (BWM) plots are used to present 2012 meteredenergyuseofthebuilding(Figs.3and4).Thisallowsquick andefﬁcientcommunicationofmanyaspectsofbuildingenergy demand,namelypeaks,medians,extremevalues(outliers)and sea-sonalvariations[13].Thebuildinghasaratherconsistentelectricity demandthroughouttheyear(Fig.3)whereonaverage, working-hourelectricalloadsﬂoatataround180kWh(withpeaksofaround 200kWh)beforefallingtoabase-loadofabout50kWhatnight.

Nightpurgeventilationstrategyraisesthenighttimebase-load to around 60kWh in midand late summer months. Daylight-linkedlightingconverselyreducesthesummermonths’electricity demandduringofﬁcehours.

Thebuildingannualheatingdemandismorevariablegiventhat itisafunctionofclimaticconditions.ExceptinJanuary,loadsabove 100kWhfallabovetheupperquartilerangeindicatingthe build-ing’swell-insulatedfabric(Fig.4).Buildingheatingrequirements arewellbelowCIBSEbestpracticerecommendations(Table1)[14]. 1.4. Softwaredescription

EnergyPlusisafirstprinciplebasedtoolandtheofficialbuilding simulationprogrammeoftheUnitedStatesDepartmentofEnergy. Itisextensivelyusedandexaminedbytheinternationalresearch communitytomodelheating,cooling,ventilation,lightingandalso waterconsumptionusingastate-spacemethod(thethermalload ofthebuildingissimulatedusingaheatbalancemethod)[15,16]. Energyplusisprimarilyasimulationengine(withnointerface)and assuchDesignBuilderversion3.2.0.067wasusedasthegraphical interface(front-endingEnergyPlusVersion7.2.0)[17].Several for-mattingstepswererequiredtoallowweatherfilestobeusedin EnergyPlusmodels,includinggenerating‘.stat’filesusing Energy-Plusweatherstatisticsandconversionsprogram.

2. Literaturereview

2.1. Modellinglimitations

Buildingmodelscaptureanarbitraryandlimitedpartofwhat essentially is a multiplicity of dynamic (fabric properties and HVAC),stochastic(occupant)andprobabilistic(weather)elements, resultinginbothinaccuracy[18,19]anduncertainty[9,20]. Soft-warelimitations, inputparameterand weatherdata inaccuracy compoundedbydifﬁcultiesin capturinghowexactlya building

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Fig.2.Sectiondrawingofthecasestudybuilding.

isoperatedarethemajorcausesofvirtualmodelerrors[21].The behaviourofheatinaspace,andthe‘coupled’sorptionand desorp-tionofmoistureintoandoutofbuildingfabric(andfurniture)are phenomenaofgreatdynamiccomplexityandquiteunderstandably onlypartiallydescribed bybuildingmodels[22]despitehaving signiﬁcant buildingenergy implications [23]. Several computa-tionalmethodsexistthatquantitativelyevaluateheatandmoisture transfer by solving the governing differential equations. These areconciselycapturedintheintroductorynotesofChen,Y.and

S.Wang[24].Limitationsofhygrothermalmodellingaredue(but notlimited)to:

1Thepropertiesofbuildingmaterialareassumedconstantinmost analytical and numerical modelsproposed tosolve heat and moisturebalanceproblems [25].In realitythepredominantly porousandhygroscopicbuildingfabricpropertieschangewith changingtemperatureandmoisturecontent,impactingheavily onHVACloadsparticularlyinhumidclimates[22].

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Fig.4.BWMplotofmeteredhourlygasconsumption(2012).

2The boundary conditions are difficult to define, and current knowledge of climatic excitation of building hygrothermal behaviourrequiresfurtherrefinement[26].

3Thehighlynonlineargoverningequationsarecomputationally difﬁcult,time-consuming[27]andpronetoerrors.

Natural ventilation and infiltration also heavily impact on hygrothermalbuildingproperties[28].Airflowrelatedelements arealsoamongthemostnotablesourcesofuncertaintyinbuilding simulation[29].Overalltheseshortcomingsinsimultaneous mod-ellingofheat,airandmoisture(HAM)formacatalogueofinquiries thatarethesubjectofongoingscientificwork[30].

2.2. Workoncalibration

Until recently calibration efforts primarily focused on how closely simulated results match the metered energy data

[10,31,32],andalsoallowedforthepersonaljudgementofthe ana-lyst[33],althougha patternofcommonconsensusis emerging towardstheapplication(andfurtherrefinement)ofASHRAEguide 14method[9,34–37].Acomprehensivecoverageofhistoricaland currentcalibrationtechniquesandtheirmeritsisavailablefrom Coakley,D.etal.[34].Inmostinstancescurrentguidelinesthatset outcriteriaonbuildingcalibrationmakeallowancesforcalibrating againstactualdataateithermonthlyorhourlyintervals[10–12]. However,callshavenowbeenmadetousehourlyvaluesof sub-meteredservicesoverannualcyclestocalibrateamodel[9,38]. Cal-ibrationworkhasmostlybeenperformedmanually[39]althougha fewmethodshavebeenproposedfortheautomationoftheprocess usinginputparameteroptimisationaswellasuncertaintyand sen-sitivityanalysis[40].Reddyidentifiesthreecalibrationmethods: firstmanualiterative,secondautomatedandfinallygraphicaland statisticalmethods[38].Mosteffortsexaminedduringthiswork centredaroundthemanualiterative methodwhich alsoallows trendingtheimprovementsmadeasaresultofinputadjustments. Mustafaraj,G.,D.Marini, etal.[41] offeroneof themostlucid effortsthat is anextension of previousworks [35,38,42].In its

comprehensiveformthisentailsatwostageversioncontrol param-eterinput(andscreening)approachthatbeginsbypopulatingthe modelwithas-builtfabric,HVACandoccupancyvalues.Inthe sec-ondstage themodel is furtherrefinedby identifyingthemost influentialinputparametersandusingfieldmeasurementdatain areiterativeprocessofadjustmenttoeventuallyestablishthefinal calibratedversion.Thismethodinformstheworkconductedhere. 2.3. Accountingforuncertainty

Whenreconcilingmeasuredandsimulatedvalues,twooverall sourcesoferrorexist.Measurementerrorcontainedintheactual dataandmodelerroremanatingfromthesimulationprocess. Mea-surementerrors can beidentiﬁed withreferencetoequipment manufacturerliteratureorbyconductingequipmentcalibration. Underpinningmodelerrorsarehowevermoreconvoluteddueto theintrinsicuncertaintiesinvolved.Severalexamplesexiststhat attempttocapturesourceandmagnitudesofuncertaintyin build-ingsimulationmodelling[43–46].Theessenceofmostofthese effortsarecapturedbyDeWit[47]whoclassiﬁesthesourcesof uncertaintyintothefollowing:

•Speciﬁcationrelated:arisingwhenthebuildingfabricand sys-temsaredescribedpartiallyorinaccurately.

•Modellingrelated: thevirtualmodel governingprinciplesare fundamentallyasimpliﬁeddescriptionofreality.

•Scenariorelated:external(e.g.climate)andinternal(e.g. occu-pant) parameters within the model are often different from reality.

Inevitablythevast volumeofinformationthatisrequiredto describea building modelgenerally leadstosimpliﬁcation and parameter reduction [48–51]. Despite this inevitability, recom-mendations are made toattempt to identifythe magnitude of modeluncertaintiessothatpredictionsofECMstudiescouldbe presentedwithgreaterlevelsofconﬁdence[52–54].Several meth-odshadbeenprovidedtoautomaticallycalibrateabuildingmodel

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while imposing certain constraints on relevant input parame-ters;including(butnotlimitedto)objectiveandpenaltyfunction techniques[55,56], which canestimate critical parameters and thenquantifythemagnitudeofuncertainty[52,54].Morerecently Bayesiantechniqueswerealsoproposedbutthesetendtobemore application-speciﬁc[57–59].Anincreasingbody ofresearchhas cometo recognisethat occupancyrelated issues actas a more prominentsourceofuncertaintyin modelpredictionsthan pre-viouslyassumed,leadingtoeffortstodevelopmostlycase-speciﬁc stochasticmodelsofoccupancy[60–67].

3. Method

The two-stage method [35,41] referred to in Section 1.2 informedthecalibrationprocessundertakenforthisstudy.A suc-cessionof19modelseach withincremental adjustmentspaved thewaytoarriveattheﬁnalversioncontaininglocal2012weather ﬁles.Againstactualhourlydata,ASHRAEGuideline14-2002was followedtocalibratethebuildingmodel[10].Thisentails deter-miningtwodimensionlessindicatorsoferrors,MBEandCV(RMSE) valuesusingformulae(1)and(2):

MBE=

Ni i=1(Mi−Si)

Ni i=1Mi (1) CV(RMSE)=

Ni i=1

[(Mi−Si)]2 Ni

1 Ni

Ni i=1Mi (2)

WhereMiandSiarerespectivemeasuredandsimulateddata

atinstancei,andNiisthecountofthenumberofvaluesusedin

thecalculation.ASHRAEGuide14considersabuildingmodel cali-bratedifhourlyMBEvaluesfallwithin±10%andhourlyCV(RMSE) valuesfallbelow30%.MBEandCV(RMSE)indiceswereconstructed overmonthlyintervalsinordertostudymonthlyvariationstoo. MBEﬁguresprovideanindicationoferrorsaveragedtothemeanof measuredvaluesbutsufferfromthecancellationeffect.CV(RMSE) indexhoweverisameasureofaccumulatederrornormalisedto themeanofthemeasuredvalues.AssuchCV(RMSE)moreclosely reﬂectstheaccumulatedmagnitudeoferrorandthereforeisa bet-termeasureoftheoverallpredictionaccuracyofthemodel.

Conventionallywhensimulatingcomplexphenomena,erroris deﬁnedasreferencevalue(observed)subtractedfromthemodel forecast(simulated) [68].Error values wereconstructedforthe threestreamsofdataunderanalysisusingequation(3):

εi=Mi−Si (3)

3.1. Parameterinputandcalibration

Prior to the handover of the building, a complete building log-bookwascompiledbythearchitectural ﬁrmwhichoutlines detailed descriptionsof as-builtfabricproperties,electrical and HVACservicedistributionandcontrolstrategies. Thisenabled a completedescriptionofthebuilding(summarisedinTable2).The estatefacilitiesmanagementsandbuildinguserswerealso con-sultedforoperationaldetails.

Theresultsofaninﬁltrationsmoketest(carriedoutat50Pa) providedtheinﬁltrationinput.Apointparticularlynoteworthyis theconversionofmeasuredbuilding’sairchangerates(expressed at50Pa)tonormalisedairleakageatatmosphericpressure.This wasachievedusingformula(4):

ACHAtmospheric≈

ACH50

F (4)

Table2

Summaryofparameterinput.

Inputparameters Value

Heating LTHWradiators+underﬂoor

heating Heatingsetpoint/setbacktemperatures 22◦_C₍₁₂◦_C)

Ventilation Displacementwithheat

recovery

HeatingsystemseasonalCoP 0.8

Naturalventilationrate(perperson) 8l/s Coolingsetpoint/setbacktemperatures 24◦_C₍₂₈◦_C)

Lighting(daylightlinkedwithlinear control)

9W/m2_(to_achieve₃₀₀_Lux)

Occupants(fromheadcount) 490(peaktime) Totalofﬁceequipmentgainsa,b ₁₀_W/m2

Occupiedhours 8am–5pm

FabricU-values

Glazing(withlowemissivitycoating) 6mmdoublepanesolarcontrol glazingwith20mmairgap (U-value1.772W/m2_K) GlazingGvalue(solartransmittance) 0.38

Externalwalls(W/m2_K) _0.292

Roof(W/m2_K) _0.25

Floor(W/m2_K) _0.13

Inﬁltration(ac/h)c _0.33

a_Computers:₆_W/m2 _(derived _from _agent-based_power _monitoring _system deployedbytheuniversityITdepartment).

b_Ofﬁce_equipment:₄_W/m2_(derived_from₁₂_electricity_sub-meters_at_the build-ing).

c _Constant_rate_of_{inﬁltration}_expressed_at_atmospheric_pressure.

WhereFisafactorusedtorelatetheairexchangeratesunder typicalconditions(ACHatmospheric)totheairexchangerateat50

Pascal(ACH50)[69].AnaverageF-valueof20wasusedasgivenby

Sherman,M.[70]. 3.2. Roomsensors

Aspartofthisprojectafirstgenerationprototypewireless sen-sorunitsweredevelopedandasetoftwoofthesesensorswere positioned1.5mabovefloorlevelandawayfromdirectsunlight rays within the 2nd floor openplan office (AppendixB). Prior to deployment thesensors were calibrated within a variety of locationswithbothstableandchangingthermalconditions. Mea-surementresultsofthetwounitswerewithin±0.3◦Cofeachother andwithin±0.5◦CofaTESTO435auditdevice.Theresponsetime ofthetwo individualsensorswerealsobelow 1min(Appendix C)whichisadequateformeasuringhighlydampedbuildingzone temperatures.Thereadingsfromthetwosensorswereaveraged toconstructtheactualzoneairtemperatureofthetargetspace (Fig.11).

4. Results

4.1. Electricity

Statistically, electrical power measurement is a continuous quantitativedatatype.Fig.5enablesaquickvisualinspectionof measuredandsimulatedvaluesandtheirstatisticalvariationsby arrangingpaireddatapointsinascendingorder.

The building’s 2012 annual electricity consumption is 910,926kWh. The ﬁnal calibrated model produced a sum of 901,059kWh (i.e. a deviation of 1.08%). Electricity carries the biggest CV(RMSE) in among the other two calibrated streams of data since electrical consumption is more closely related to occupant activitythat deviates(ina randommanner) fromthe deterministic occupancy templates used in Energyplus model. Hourly MRE andCV(RMSE) indicesfor individualmonths show thatDecembercarriesthebiggestmonthlyaccumulationoferrors

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Fig.5.Hourly-basedmeasuredandsimulatedbuildingelectricityconsumptioninascendingorder(kWh). (Fig.6).Thefacilitiesmanagerinthetargetbuildingalsotakesa

veryproactiveroleandthesetpointswithinthespaceareregularly updatedinresponsetooccupant’scomments.Thisaddsagreater probabilisticpattern toactualbuildingperformanceasopposed tothe static and template-driven nature of simulation results. Lighting,smallpowerandHVACplantelectricalconsumptionare sub-meteredbutavailableonmonthlyintervalsonlyandtheﬁnal modelMBEand CV(RMSE)valuesformonthlydatawerewithin ASHRAErecommendedmonthlyvalues(Table3).

HVACenergyconsumptioncarriesthebiggesterrorinthethree categoriesforwhichmonthlysub-meteredvalueswereavailable.

Table3

Sub-categorisedCV(RMSE)andMBEvalues(monthlydata).

Category BE* (monthly) V(RMSE)** (monthly) V(RMSE)*** (hourly) HVAC 4.69 13.1 – Lighting −2.62 9.1 – Smallpower −1.6 6.3

Overallelectricityconsumption 0.50 9.7 9.9

*_Acceptance_limit:₋_5%_≤_MBE_(monthly)_≤_+5%. **_Acceptance_limit:_CV_(RMSE)_(monthly)_≤_+15%. ***_Acceptance_limit:_CV_(RMSE)_(hourly)_≤_+30%.

0.9% 9.9% -10% -5% 0% 5% 10% 15% 20% 25% MBE CV(RMSE)

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40 30 20 10 0 -10 -20 -30 -40 900 800 700 600 500 400 300 200 100 0 Residual value (kWh) Fr e q u e n cy

Fig.7. Histogramofhourlyelectricityresiduals(kWh).

Lightingandsmallpowerwerebothslightlyover-predictedbythe modelwhereasHVACwasunder-predicted.Notethatconsistent manufacturerseasonalefﬁcienciesforHVACplantswereusedand noadjustmentsweremadetoachievecloserresults.

Quite clearly EnergyPlus electricity prediction surpasses the calibration criteria set by the ASHREA guide 14. However the cancelling effect of MBE values are evident given that for instancetheoverallMBEhourlyﬁgureof0.9%ismuchsmallerthan

CV(RMSE)houlryvalueof9.9(Fig.6).

Thehistogramofhourlyresidualsprovidesaquantiﬁed illus-tration of themagnitude and spread of model electrical errors (Fig. 7). Hourly electrical errors (or residuals) form a bi-modal

chartwithoverallcharacteristic ofnormal distributioncentring onzero.Expressedin relativeterms,94%of errorshavea mag-nitudefallingwithin±10%ofdailypeakelectricityconsumption (i.e.±20kW).Theextremeincidentsofnegativeerrors(i.e.model over-predicting)haveasimilarmagnitudetopositiveerrorsand thefrequencyofbothinstancesarelow(only1.1%oferrorsare largerthan±30kWh).

4.2. Gas

Spaceheatinginthecasestudybuildingonlyoccurswhenthe averagedailyoutdoortemperatureisbelow11.5◦C,conﬁrmingthe well-insulatednatureofthebuildingfabric. Theheatingsystem (fortheyearunderexamination)wasalsoentirelyshutdownfrom mid-JunetoearlyOctober.Thepairedinstancesofmeasuredand simulatedgasconsumption,againanothercontinuousquantitative datatype,arearrangedinascendingorderinFig.8.

Abuilding’sheating-relatedgasconsumptionisadirectfunction ofoutdoortemperatures.Astheweatherdatausedinthe simula-tionprocesswasgeneratedusingthebuilding’srooftopweather station,thesimulatedandmeasureddatapredictablybearavery closeresemblance.APearsoncorrelationﬁgureof99.7%indicates thatmeasuredandsimulatedgasconsumptionvaluesvaryclosely inmagnitudeanddirection.Themeasuredenergyconsumptionin

Fig.8hasa steppedpattern duetothefactthat thethree boil-ers serving thebuildingcannot modulateinfinitely, sothere is repetitionof specificpart/full loadcapacity outputsat timesof similarheatdemand.Thesimulatedvalueshoweverresultfrom mathematicalloadcalculation,whichwouldbydefinitionhavean infinitelyvariablenature.Fig.9outlinestheMBEandCV(RMSE) cal-ibrationresults.Aswellasmonthlyandannualvalues,theoverall

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Fig.9.MBEandCV(RMSE)analysisforbuildinggasconsumption(hourlydata).

resultsarecalculateddiscountingthesummermonthsto demon-stratethemoderatingeffectthat3monthsofnoheatingload(i.e. zerogaspredictionerrors)canhaveontheoverallresult.Thelargest errorsbelongtoOctoberseasonwhenthebuildingwasalsoused attheweekendsfororganisationalpurposes.

TheMBEandCV(RMSE)valuesfallwithintherespectiveASHREA acceptancelimitsofwithin±10%andbelow+30%.Equally91.1% oftheresidualvaluesfallwithin±10kWh(Fig.10).Thesimulated modeldisplaysaslighttendencytoover-predictgasconsumption (hencegreaterincidentsofnegativeresiduals).Thefrequencyof bothoverandunderpredictedvaluesarehoweverinsigniﬁcant. Notethatthelargeincidentsofvaluesbinnedatthebarcentring onzero(Fig.10)isduetothreemonthsofnoloads(henceresidual valueofzero)aswellasincidentsofnoboileroperationduring heatingseason.

4.3. Spacetemperature

Actualrecordedairtemperatureoverafullannualcyclewere usedtoexaminetheabilityofEnergyPlusmodeltoaccurately pre-dictzonetemperature.Thisalsoactsasastage2calibration.Fig.11

allowsaquickvisualcomparisonoftheBWMplotsofannualhourly spaceairtemperaturesforbothmeasuredandsimulateddatasets.

Fig.12outlinestheMBEandCV(RMSE)errorchecksof tempera-tureresults.ItwasnotedearlierthatMBEandCV(RMSE)indices are statistical gauges of normalised ‘relative’ and normalised

30 20 10 5 0 -5 -10 -20 -30 3500 3000 2500 2000 1500 1000 500 0 Residual values (kWh) Fr e q u e n cy

Fig.10.Histogramofhourlygasresiduals(kWh).

‘accumulated’ errors and as such they can offer insights into deviationsofEnergyPlusspacetemperaturepredictionfrom corre-spondingactualvalues.99.5%oftheerrors(asdeﬁnedbyformula 3)fallwithin±1.5◦Cand 93.2%arewithin±1◦C.Overall actual spacetemperaturesarewarmerbyanaverageof0.47◦Coverthe fullannualcycle,andthisisevidentbythegreaterinstancesof positiveerrors onFigs. 13and14.ThehistograminFig.13 has a bimodal spread with its centre at around 0.25◦C, indicating thattheEnergyPlusmodelinthiscasestudytendstomarginally under-predictthetemperatureinthespace.Errorshoweverhave anequaldistributiononbothsidesofthepeak.Thescatterploton

Fig.14showsaconstantspreadoferrorswithincreasingsimulated temperatures,whichdemonstratesthatEnergyplusmodel main-tainsaconstantlevelofaccuracyacrossthefullrangeofpredicted temperatures.

Thesummerperiod(withtheheatingsystemidle)observesa smallreductionin themagnitudeofMBEand CV(RMSE)values (Fig.12).Converselyallwintermonthshaveslightlylargererror magnitudes.Thistrendsuggestsamoreaccuratetemperature pre-dictionbythemodelintheabsenceofheatingsysteminput(When AHUsareonlydeliveringdisplacementventilation).HVACheating operationthereforeintroducesalargerelementoferrorinmodel temperaturepredictionsin this study.Recallfrom sub-metered electricalcalibrationresultsthat HVACcategoryhad thelargest magnitudeoferror(Table3).

12 11 10 9 8 7 6 5 4 3 2 1 28 26 24 22 20 18 16 12 11 10 9 8 7 6 5 4 3 2 1 Measured Month °C Simulated Measured vs Simulated

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0.74% 1.96% -5% -3% -1% 1% 3% 5% MBE CV(RMSE)

No heang plant operaon

Fig.12.MBEandCV(RMSE)analysisofofﬁcetemperature(hourlydata).

2.0 1.5 1.0 0.5 0.0 -0.5 -1.0 -1.5 -2.0 600 500 400 300 200 100 0 Residual Value (°C) Fr e q u e n cy

Fig.13.Histogramofhourlytemperatureresiduals.

Fig.14.Hourly-basedscatterplotofresidualversussimulatedvalues(◦_C).

5. Discussions

Giventhediverseandvariednatureofunderlyinguncertainties insimulationattempts,buildingperformanceresultscanatbest restwithinasmallallowableerrormargin.Intheﬁrststageofthe calibration,adetailedEnergyPlusbuildingmodelcontaininglocal weatherdataachievedrespectivehourlyMBEandCV(RMSE)values of±5%andbelow+10%forgasandelectricityloadprediction.Inthe secondstagethecalibratedmodeldemonstratedairtemperature predictionaccuraciesof±1.5◦Cfornearly99.5%andanaccuracy of_±1◦Cfornearly93.2%ofhourlyinstancesoverthefullannual cycle.MBEandCV(RMSE)valueseachprovidea differentsetof insights.MBEvalueshavethedrawbackofcancellationandhence

mightunder-reportthemagnitudeofseasonalerrors,asobserved forinstanceinelectricalcalibrationwheretheoverallMBEvalueof 0.9concealedmuchlargermonthlyMBEerrors(Fig.6).Therefore,if theanalystseekstohighlightinstanceofunderoroverprediction, monthlyintervalsofMBEindexcanprovemoreinstrumental.This thereforeallowstheanalysttheopportunitytoaccountfor sea-sonalvariationsandreasonsbehindthem.IncontrastCV(RMSE) valuesprovideabetterindicationifasingleindexdemonstrating the‘accumulatedmagnitude’oferrorissought.Withinthiswork theHVACenergyconsumptioncarriedthelargesterrorandwas thegreatestsourceofuncertaintyinthemodelenergyprediction, affectingmostlythesimulatedelectricityvalue.Incontrastmodel gasconsumption prediction(beinga directfunction ofweather data)achievedgreaterlevelsofaccuracy.Interestinglyactualgas consumptionwassmallerthansimulated(Fig.8),yettheactual HVAC electricityconsumption wasbiggerthan simulated(from

Table3,ElectricityMBEMothlyvalueforHVACconsumptionhasa

positive magnitude of4.69which points tolargeractualHVAC electricityconsumption).Sincelessgasconsumptionshouldlead tosmallerheatingrelatedpumpingduties(hencesmallerHVAC electricity),thisdiscrepancycouldariseform:

1ThetwolargeAHUsandthecoldwaterboosterpumpsets. 2Naturalventilationandinﬁltrationvalues whichcouldleadto

largersummer-timecoolingload.Airflowsinbuildingscontinue toremainverydifficulttomeasureandquantifydespitetheir significantenergyandcomfortimplications.

Deriving more definite conclusions in the absence of sub-meteredwaterandhourlyAHUfigureswouldhowevernotcarry much scientific rigor.Another notablepoint is that in order to accountforthemeasurement errorwithinthiscalibration exer-cise,authorshavemadetheassumptionthatthegasandelectricity meter’saccuracywithintargetbuildingcomplieswithSI684(1983) andIEC62053respectivelyasextensiveattemptstoobtainmeter compound errormarginsfrom manufacturersfailedtoproduce anyresults.Theseguidelinesallow+2.5%or−3.5%ofcompound instantaneousdeviations.

Overall Energyplus engine provided a very accurate evalua-tionofbuildingenergyandenvironmentalperformancewhereby annual simulated electricity load was under-predicted by only 1.08%, thatof gaswasover-predicted by3.8%and annual tem-perature withinthespace wasunder-predictedby 0.47◦C. It is hopedthatthisattemptcanofferinsighttofurtherreﬁnementof thecalibrationprocesswherecurrentlyclariﬁcationsare particu-larlyneededforinstanceswhenmeasureddataislimitedorofa coarsenature.Itcouldbearguedthatthebulkofexistingbuilding

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errors.Pervasive sensing technologiesand digitallylogged sub-metered information offer the re-focusing the post occupancy studiestotheexploitationofactualvaluesthatwithinacalibrated modelcanfacilitateECMandoptimisationstudieswith increas-ingaccuracy.Thefollowingsummarisesthemainﬁndingsofthis workthatoffersasetofrecommendationiffurtherdevelopment ofASHRAEmethodofbuildingenergymodelcalibrationwastobe undertaken:

1Wherepossiblecalibrationshouldbeconductedoveranannual cycleusinghourlyenergydata.Whereimpractical;hourly pri-mary data could be collected for shorter cycles (weekly or monthly)to‘validate’simulationresults.

2Localweatherﬁlesshouldbeobtainedandusedforamodelto beconsideredcalibrated.Anyotherweatherﬁletypemayassist avirtualmodeltobevalidated.

3MBE and CV(RMSE) calibration results (as those inferred by ASHRAEGuide 14)when presented inmonthly intervalswill allowanassessmentofseasonalvariations.

4Residualhistogramsorscatterplotscanalsoshedfurtherlighton thetendencyofthemodeltounder/overpredictacrossthefull rangeofsimulatedvalues.

5Thelevelsoftoleratederrorofa modelshouldbedictatedby thefunctionofthevirtualmodelandprimarydataavailability. Thereisscopeforfurtherworktodefinetherequiredlevelsof modelaccuracyforeffortssuchasoptimisationandcontrol stud-ies,ECMandtechnologyappraisals,renewableintegration,etc. Tothatend,furtherrefinementofcalibrationguidelinesshould firstreflectthemodelpurpose.

Fig.A1.Casestudybuilding’srooftopweatherstation(insetleft:twomain dash-boarddesktopinterfacesprovidinglivereadings).

Intheirconcludingremarks,Raftery,Petal.recommend nar-rowing ASHREA acceptance criteria hourly MBE and CV(RMSE) values to ±5% and ≤+20% respectively [72]. As demonstrated withinthiswork,modelscalibratedtotheselimitscanmore confi-dentlypredictactualprevailingtemperatureswithinthebuilding. Thefindingsofthis papersupportsthisproposition particularly forscientificworkconductedusingactualhourlydataoverannual cycles.Thiswould ensuregreaterconfidencein theaccuracyof modelbasedstudiesandbringsaboutaunifiedapproachtomodel calibration.TheexistingMBEandCV(RMSE)valuesof±10%and ±30%canstillbeadheredtowhencompleteannualhourlydatais notavailabletotheanalystandsuchamodelcanbeconsidered ‘validated’.

Inafollow-uppaperthecalibratedmodelisusedtoexaminethe potentialsofadaptivecomfortasdeﬁnedbyEN152512007inthe targetofﬁcebuildingwithincurrentandpassivehouseenvelope.

Acknowledgements

TheauthorswouldliketothankEngineeringandPhysical Sci-encesResearchCouncil(EPSRC)fortheirﬁnancialsupportwhich enabledtheundertakingofthisresearchwork(EPSRCgrant refer-enceEP/I000755/1).

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10 15 20 25 30 35 40 0 10 20 30 40 50 60 0 10 20 30 40 50 60 °C Time (sec) Sensor 1 Sensor 2

Fig.C1.Sensorresponsetimecurvesoverfullroomtemperatureranges.

0 5 10 15 20 25 30 35 3 10 20 30 Ca librat or [T ES TO 435 ] (° C) Calibrated sensor (°C) Sensor 1 Sensor 2

Fig.C2.AccuracygraphproducedagainstTESTO435measurements.

AppendixA. Weatherstation

Fig.A1

AppendixB. Sensorpositions

Fig.B1

AppendixC. Sensorcalibrationresults

Figs.C1andC2.

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