RACE for Business
Research Theme: Digitalising Industry BT1
ISBN: 978-1-922746-66-5
Industry Report
Data requirements and feasibility of unlocking flexibility in commercial buildings
Citations
Liebman, A., De Nijs, F., Labouchardiere, M., Xie, Y., Salim, M., Patankar, P., Lalbakhsh, P. (2024). AI-ready flexible buildings. Prepared for RACE for 2030 CRC.
October 2024
Projectteam Universitypartner
•Prof.ArielLiebman†
•Dr.FritsdeNijs
•Dr.MarcelLabouchardiere
•Dr.YitingXie
•MariamSalim
•PratoshPatankar
•Dr.PooiaLalbakhsh
† DeceasedNovember2023
BuildingsAlive
•Dr.CraigRoussac
•Dr.BradSchultz
Project partners
Acknowledgements
Theresearchteamwouldliketothanktheindustryreferencegroupparticipantsfortheirfeedbackandinsights onthework.
WeacknowledgetheuseofgenerativeAItoolsChatGPTandGrammarlytoimprovethetoneandclarityofthis report.
AcknowledgementofCountry
TheauthorsofthisreportwouldliketorespectfullyacknowledgetheTraditionalOwnersoftheancestrallands throughoutAustraliaandtheirconnectiontoland,seaandcommunity.Werecognisetheircontinuingconnection totheland,waters,andcultureandpayourrespectstothem,theirculturesandtotheirElderspastandpresent.
WhatisRACEfor2030? RACEfor2030CRCisa10-yearco-operativeresearchcentrewithAUD350millionofresourcestofundresearch towardsareliable,affordable,andcleanenergyfuture. www.racefor2030.com.au
Disclaimer
Theauthorshaveusedallduecareandskilltoensurethematerialisaccurateasatthedateofthisreport.The authorsdonotacceptanyresponsibilityforanylossthatmayarisebyanyonerelyinguponitscontents.
1 Introduction BuildingsplayasignificantroleinenergyconsumptionandtheemissionofCO2 worldwide,accountingfor approximately40%oftheworld’senergyconsumptionandcontributingtoabout35%oftotalgreenhousegas emissions(Ürge-Vorsatzetal., 2015;Magnietal., 2021;Pouranian,Akbari,andHosseinalipour, 2021;X.Zhang etal., 2020;Pervez,Ali,andPetrillo, 2021).TheNetZeroroadmapbytheInternationalEnergyAgency(2021) suggeststhattoachieveglobalnet-zeroemissionsby2050,heatingandcoolingenergyuseshoulddropfrom 100%in2020to50%by2030andfinally20%in2050.Theroadmapexpectsthat efficiency istheprimarydriver oftherequiredreductioninbuildings’totalfinalconsumption,withananticipated350MtCO2 reductionby2050 achievedthroughdigitalisationandintelligentcontrols.Deployingsuchintelligentbuildingcontrolstoretrofit existingbuildingsisonepotentiallevertoquicklyreducinggreenhousegasemissions.
Heatingandcoolinginofficebuildingsaremajorcontributorstoenergyconsumption,accountingforasignificant percentageofbuildingenergyusageandCO2 emissions.Whilethisisasignificantoutlay,buildingsalsohavethe potentialtoabsorb,store,andreleasetheheatingorcoolingintothebuildingmaterials,muchthesamewaya batterydoes.Bypre-coolingorpreheatingthebuilding,“thermalinertia”will,intheory,providetheflexibilityto usemorevariablerenewableenergy,thusloweringthecarbonemissionsofthebuilding.However,inpractice, thiswillrequiregreaterautomationandresponsivenessofthebuildingstoclimate,renewableresources,and electricitygridprices.
Thisprojectaimstodefine,test,anddocumentthedataproducedbynewautomatedsystemstodeterminethe optimaltimestoheatandcoolabuilding.ThisdecisionprocesswillalsobeautomatedbycombiningAI-driven predictivemodellingwithstrategicdataorganisationandinnovativeoptimisationtechniquestosignificantly reduceenergyusageandcarbonemissionsinofficebuildingswhilemaintainingcomfortandproductivity.
Thecoreoutputoftheprojectisasimulationmodelofanofficebuilding(modelledonanexistingofficebuildingin MonashUniversity),forwhichweoptimiseanHVACcontrolscheduletomakethebestprecoolingandpreheating decisions.Optimisingthecontrolofthebuildingautomationandmanagementsystem(BAMS)toaligntheenergy consumptionwithperiodsoflowcarbonintensitygridpower,weareabletoreducethecarbonintensityofthe building’soverallenergyrequirementsby10%.ExtrapolatingthisresulttotheentireMonashcampussuggestsa potentialannualreductionofupto23,000tonsofCO2-eifthissolutionisfullydeployedacrossallbuildings, althoughfurtherreal-worldstudiesareneededtovalidatethesimulationestimates.
Theproject’slong-termbenefitsincludegreateraccessibilityofthebuildingcontroldataforR&D,developing advancedcontrolmodelsforenergyoptimisation,significantlyreducingcarbonintensityinbuildingoperations, andsettingaprecedentforfutureinnovationsinsustainablebuildingmanagement.
Literaturereviewonbuildingautomation Thischapterprovidesacomprehensiveintroductiontothefoundationalconceptsofbuildingautomation, emphasisingthegrowingsignificanceofdata-drivenstrategiesinbuildingenergymanagementacrossboththe designandoperationalphases.Thesesystemscanbeusedforarangeofautomationandmanagementtaskssuch asloadforecasting(M.Huetal., 2023),anomalyandfaultdetectionanddiagnosis(Leietal., 2023),predictive controlandthermalcomfort(G.HuandYou, 2023),indoorenvironmentalquality(IEQ)monitoring(Majdietal., 2022),facilityandassetmanagement(OgollaandKieti, 2022),energyefficiencyplanning(Verma,Prakash,and Kumar, 2023),securityandsafety(HsiaoandHsieh, 2023),occupancydetection(Sayed,Himeur,andBensaali, 2022),andwaterusagemanagement(AlGhamdiandSharma, 2022).
Whiletraditionalapproachesrelyonphysicalmodellingtoaccountforfactorslikeshading,passiveheating, orientation,naturalventilation,andgeometry,thedata-drivenapproachharnessesempiricaldatatoextract valuableinsights.Byanalysingdatarelatedtoheating/coolingload,lighting,electricityusage,andoccupancy profiles,amoreaccurateunderstandingofbuildingperformancecanbeachieved(PanandL.Zhang, 2020; Che-AniandRaman, 2019).Collectingandmaintainingreal-worldbuildingenergyusedata(RBEUD)thatrepresent theactualconditionofbuildingsenhancesoperationalefficiencyandcontributestovaluableknowledgeforfuture projects.Adoptingmoderndistributedcomputingplatforms,suchascloudcomputing(Mohamed,LazarovaMolnar,andAl-Jaroodi, 2016),edgecomputing(Minhetal., 2022),fogcomputing(Iftikharetal., 2022),and hybridstrategies(Himeuretal., 2022),cansimplifyandsupportthistaskbyprovidingseamlessconnectivity,data pre-processingandstorage,andpowerfulcomputingresourcesatvariouslevels.Theavailabilityofsuchdatasets playsapivotalroleininformedpolicy-makingandbridgingtheenergyperformancegap(EPG),whichrepresents thedisparitybetweenmandatedenergyconsumptionandactualusageinpractice(X.Wangetal., 2023).
2.1 TraditionalHVACControl TraditionalHVACcontrolmethodsfocusonmaintainingindoorenvironmentalcomfortbyregulatingtemperature, humidity,andaircirculationusingsetpointandfeedbackcontrolmechanisms.Theseconventionalstrategies includemanualadjustments,whereoccupantsadjustsettingsbasedoncomfortlevels,andautomatedsystems, whichrelyonpredefinedsetpointsfortemperatureandhumiditycontrolandoperateindependentlytomaintain aspecifiedcomfortrange.
Significantenergysavingsandpreservationofhumancomfortcanbeachievedbyoptimisingthesecontrol strategies,includingtheuseofoptimalcontrolstrategycurvesandoperationcurvesforequipmentanddevices. Moreover,theadventofintelligentcontroltechniquesanduser-adaptablecomfortcontrolsystemssignifiesa shifttowardsmoreresponsiveandenergy-efficientHVACoperation,aimingtoalignmorecloselywiththespecific thermalcomfortpreferencesofoccupants(Federspiel, 1992;Mirinejad,Welch,andSpicer, 2012).
Historically,automation,controlsystems,andmaterialscienceadvancementshavebeenthefocalpointsfor improvingHVACefficiency(Aftabetal., 2017).However,therecentintegrationofAIandmachinelearning technologiesandaglobalshifttowardsrenewableenergysourceshavesignificantlyenhancedHVACoperations. ThistechnologicalevolutionreducestheenvironmentalfootprintofHVACsystemsandrevolutionisescontrol strategiesandmethodologies. 4
2.2 AIandMachineLearningforHVACControlStrategiesandAlgorithms RecentstudiesunderscoretheeffectivenessofleveragingpredictivecontrolalgorithmsanddeeplearningapproachesforoptimisingenergyconsumptionandenhancingthermalcomfortwithinbuildingsthroughHVAC systems(Kalaimani,Keshav,andRosenberg, 2016).Furthermore,thedevelopmentofintelligentbuildingautomationsystems,capableofdynamicallysensingoccupantcomfortneedsandaccordinglyschedulingHVAC operations,representsasignificantadvancementtowardsmoreefficientandresponsivebuildingenvironments (Aftabetal., 2017).
Buildings,particularlythroughtheirHVACsystems,canenhancegridstabilitybyadjustingtheirelectricloadsin responsetosignalsfromthegrid.ThisadjustmentcontributestomaintainingtheNetworkedEnergyManagement (NEM)frequencybalanceandoverallsystemstability.Byimplementingcontrolstrategies,HVACsystemsin bothcommercialandresidentialsettingscanofferfrequencyregulationserviceswithoutsignificantlyimpacting indoorclimatecomfort.Researchhasexploredmodelpredictivecontrolandrule-basedcontrolstrategiesthat enableHVACoperationstobedynamicallyadjusted,takingintoaccountfactorslikeoccupantcomfortand hardwareconstraints.Suchstrategiesallowforamoreresponsiveandenergy-efficientoperationofHVAC systems,providingvaluableancillaryservicestothegrid,suchasdemandpeakreductionandbalancingsupply anddemand,therebysupportingastableandefficientenergysystem.
2.3 AI-DrivenHVACControl—Buildingsthatactasbatteriesoflastresort Theintegrationofrenewableenergysourcesintotheelectricitygridhasledtotheneedfordemandresponse strategiestomitigatethevolatilityofrenewableenergysupply.HVACsystems,particularlyincommercialand industrialsettings,havebeenidentifiedaspotentialcandidatesforfast-demandresponseapplications(Goddard, Klose,andBackhaus, 2014).Thepotentialofdemandresponseinenablingflexibilityforhigherrenewable energypenetrationandefficientresourceexploitationhasbeenhighlighted,particularlyinindustrialnear-zeroenergybuildings(Kampelisetal., 2019).Additionally,researchhasfocusedonintegratingHVACoperationinto demandresponseprograms,emphasisingtheneedforoccupant-orienteddemandresponsewithroom-individual buildingcontrol(Frahmetal., 2023).Furthermore,anintegratedapproachtoadaptivecontrolandsupervisory optimisationofHVACcontrolsystemsfordemandresponseapplicationshasbeenproposedasaneffective solutionforascalableandadaptabledemandresponseplatformforHVACsystems(Adegbenro,Short,and Angione, 2021).
Inthecontextofmicrogrids,cooperativealgorithmsutilisingHVACdemandresponsehavebeenexploredto minimisesupply-demandmismatch,therebyreducingtheneedforenergystoragedevices(J.Ma,X.Ma,and Ilic, 2019).Moreover,controlstrategiesforcoordinatingtheoperatingschedulesofmultipleHVACdevicesin residentialdemandresponsehavebeeninvestigated,emphasisingtheinseparabilityofeffectivecontrolalgorithms fromresidentialdemandresponsebasedonHVACsystems(Kouetal., 2021).Thesestudiescollectivelyunderscore thepotentialofHVACdemandresponsetoaddressthechallengesposedbyintegratingrenewableenergysources intotheelectricitygrid.
Inresponsetogridshortagesoroutages,buildingscancollectivelyactas“batteries”toprovideloadshedding orself-curtailedservicesbyadjustingtheset-pointsofHVACsystemswithinreasonableranges.Thisadaptive managementofHVACsystemsenablesbuildingstoreducetheirelectricloaddynamically,contributingtogrid stabilityduringpeakdemandtimesorinsituationswherethegrid’ssupplycapacityisstrained.Conversely, whenthegridexperiencessurplusenergyavailabilityduetofavorableconditionsforphotovoltaic(PV)orwind generation,buildingscanincreasetheirHVACconsumptionflexibly.Bydoingso,buildingscanabsorbexcess
energy,preventingpotentialwasteofrenewableresourcesandaidinginthebalancingofsupplyanddemandon thegrid.
Forinstance,researchhasproposedtheconceptofaVirtualBattery(VB)controlforcommercialHVACsystems toadjustpowerconsumptioninreal-timebyregulatingzonalairflowrates,demonstratinghowbuildingscan effectivelyrespondtogridsignals.Additionally,theintegrationofHVACandbatteryschedulinginbuildings hasbeenexploredtooptimisedemandresponse,showcasingthepotentialforbuildingstomanagepeakload demandswhileensuringthermalcomfortandleveragingbatterystorage.
Theseapproacheshighlightthepivotalrolebuildingscanplayinenhancinggridresilienceandstability.By leveragingHVACsystems’flexibilityalongsideenergystoragesolutions,buildingscaneffectivelyserveasdynamic assetsinthesmartgrid,adjustingtheirenergyprofilestosupportgridoperationswhilemaintainingoccupant comfort.
2.4 BuildingAutomationandManagementSystems Abuildingautomationandmanagementsystem(BAMS)isasophisticatedinstallationwithinbuildingsthatplays acrucialroleinoverseeingandregulatingadiverserangeofbuildingservices.Theseservicesincludeheating, cooling,ventilation,airconditioning,lighting,shading,lifesafety,alarmsecuritysystems,andmore.Atitscore,a BAMSaimstobringautomationtotechnologically-enabledenvironmentsbyharmonisingamultitudeofelectrical andmechanicaldevices.Thesedevicesareintricatelyconnectedthroughdistributedcontrolnetworks,forming aninterconnectedecosystem.BAMSsfindwideapplicationacrossvarioussettings,rangingfromindustrial facilitiesandcommercialbuildingstobustlingmallsandevenresidentialproperties(Dominguesetal., 2016). Whilebuildingautomationandmanagementsystemstheoreticallyhavethepotentialtoprovidecomprehensive componentsandfunctionalitiesforanalysingandoperatingbuildings,thereareadditionalvitaltasksthatoften fallundertheresponsibilityoftheoperator.Inotherwords,energymanagementsystemscanbeconsidered asreactivesystems,therefore,itisnecessarytodevelopsmarterstrategiesthatarefullyfocusedonenergy improvement(Lee,Cha,andPark, 2016).Thesetasksandstrategiesincludeevaluatingbuildingperformance, detectingunusualenergyconsumptionpatterns,identifyingefficiencyimprovements,andensuringthesecurity andprivacyofend-users(Himeuretal., 2022).
Sincetheearly2000s,theapplicationofvirtualisationindatacentreshasbroughtaboutaparadigmshiftindigital systems,deliveringremarkableimprovementsinflexibility,availability,andsecurity.BAMSshaveembracedthis transformativetrendbyseamlesslyintegratingwirelesstechnologieslikeWiFi.Thisintegrationhasempowered BAMSswiththeabilitytoenableremoteaccessandmonitoring,unlockingnewopportunitiesforefficient andeffectivemanagementofbuildingsystems(S.Wang, 2009).Furthermore,thisintegrationhaslaidasolid foundationfortheimplementationofcutting-edgealgorithmsandtechnologiesthatsupporthigher-leveltasks. Thesetasksrequiretheutilisationofadvancedtoolssuchasbigdataanalyticspipelines,capableofprocessingvast amountsofinterconnectedequipmentdata.Additionally,artificialintelligenceandmachinelearningalgorithms playacrucialroleintaskssuchasloadforecasting,watermanagement,indoorenvironmentalqualitymonitoring, occupancydetection,andenergyanomalydetection.Byleveragingtheseinnovativeapproaches,building automationsystemscollaboratetoenhancetheoverallperformanceofthebuilding,ensuringoptimalefficiency, occupantcomfort,andsustainability(Debrah,Chan,andDarko, 2022).
ThesignificanceofBAMSsliesintheirabilitytoensureoccupantsatisfaction,reduceenergyconsumption,and streamlineefficientbuildingoperations.Toachievethis,BAMSsprovidecomprehensiveawarenesstorelevant managers,whichissupportedbythecollectionofhigh-resolutiondata.Thisdataisacquiredthroughadiverse
arrayofsensorsthatarestrategicallydeployedandconnectedtotheBAMSusingdifferentcommunication technologies,suchasfieldbusandIoTstandards(Pandya, 2021;Moudgiletal., 2023;Zaerietal., 2022).By harnessingthesetechnologicaladvancements,BAMSsempowerbuildingmanagerstomakeinformeddecisions andoptimisebuildingperformanceinpursuitofenergyefficiency,occupantcomfort,andsustainableoperation.
Thefieldofbuildingautomationhaslongbeendominatedbyawidearrayofproprietarysolutions,withad-hoc approachesoftenmeetingmoderateperformancerequirements.However,drivenbythegrowingmarketdemand foropensystems,evenindustryleadersaregraduallyshiftingawayfromproprietarydesigns.Recognisingthe importanceofopensystems,officialstandardsbodiesareactivelyinvolvedinensuringthatthestandardsthey developandpublishadheretotheprinciplesofopenness.Theseprinciplesincludenon-discriminatoryaccess tospecificationsandlicensing.Consequently,theadherenceofequipmenttoformalstandardsisincreasingly becomingarequirementinmanyprocurementprocesses.Standardsdirectlyrelevanttobuildingautomation systemtechnologyaredevelopedbothintheUnitedStatesandbyseveralEuropeanandinternationalstandardsbodiessuchasISO1,CEN2,andCENELEC3.Thesestandardsserveasguidelinesandbenchmarksforthe industry,promotinginteroperability,compatibility,andtheseamlessintegrationofdifferentbuildingautomation components(Kastneretal., 2005).
Kastneretal.(2005)presentageneralsystemmodelthatencapsulatesmosttypesofBAMS,byseparatingout thefunctionalityofitsindividualcomponentsacrossthreelevels:
• FieldLevel:Thefieldlevelcoverstheswitches,sensorsandactuatorsthattheBAMSisultimatelyinteracting withtoachievethedesiredreal-worldoutcomesinthebuilding.Throughthefieldlevel,theBAMSinterfaces withthephysicalworldinadistributedmanner,collectingandtransformingmeasurement,counting,and meteringdataintoaformatsuitablefortransmissionandprocessing.Additionally,thislevelfacilitates physicalcontroloverenvironmentalparametersthroughactionssuchasswitching,setting,andpositioning inresponsetosystemcommands.
• AutomationLevel:Attheautomationlevel,awiderangeofautonomouslyexecutedsequencescomeinto play.Thisleveloperatesonthedatapreparedbythefieldlevel,establishinglogicalconnectionsandcontrol loopstofacilitateefficientoperations.Theautomationlevelservesasacrucialintermediarybetweenthe fieldlevelandthemanagementlevel,providingthenecessaryintelligenceanddecision-makingcapabilities forthesmoothandefficientoperationoftheoverallsystem.
• ManagementLevel:Atthemanagementlevel,comprehensiveaccesstoinformationfromtheentiresystem ismadeavailable.Thislevelpresentsaunifiedinterfacetotheoperator,facilitatingmanualintervention whennecessary.Itprovidesverticalaccesstoautomation-levelvalues,allowingforthemodificationof parameterssuchasschedulestoadapttochangingrequirements.Furthermore,themanagementlevel playsacrucialroleinmonitoringsystemhealthandgeneratingalertsforexceptionalsituationsliketechnical faultsorcriticalconditions.Italsoencompasseslong-termhistoricaldatastorage,enablingthegeneration ofreportsandstatisticsforin-depthanalysisanddecision-making.
ItisimportanttohighlightthatdeviceswithinBAMSoftenincorporateacombinationoffunctionalitiesfrom allthreelevels.Thisamalgamationofservicesandrequirementsnecessitatesnetworkarchitecturesthatcan accommodatethisdiversity.Strictlyadheringtoathree-tiernetworkstructurewouldintroduceunnecessary
1InternationalStandardsOrganization
2EuropeanCommitteeforStandardization
3EuropeanCommitteeforElectrotechnicalStandardisation
complexitywhenitcomestosharingdevices,especiallysensors,betweendifferentfunctionaldomains(Kastner etal., 2005).
HerearethemaincomponentscommonlyfoundinaBAMS:
• Sensors:Thedevicesresponsibleforcapturingdataonvariousaspectsofthebuildingenvironment,such astemperature,occupancy,lightlevels,andairquality.SensorsprovideinputstotheBAMS,enabling effectivemonitoringandcontrol.Leveragingadvancedsensingandmeteringtechnologies,datacanbe gatheredfrommultiplemodalities,creatingacomprehensiveinformationsourcefortargetedanalysisand thedevelopmentofintelligentandsophisticatedtools.Measurementdevicesincreasetheobservabilityof thebuilding’stransientanddynamiceventsaswellasgatheractualdatarelatedtodiversefunctionalities ofthebuilding(Himeuretal., 2022).Sensorsareeitherdirectlyconnectedtocontrollersviaastandard interfaceorbymeansofafieldnetwork(Kastneretal., 2005).
• Controllers:Controllersareresponsibleforprocessingthedatareceivedfromsensorsandinitiating appropriateactions.Theymakedecisionsbasedonpredefinedalgorithmsandcontrolstrategiesto regulatebuildingsystems.ThecontrolunitisthecentralbrainoftheBAMSgenerallybuiltuponindustry standardsandprotocolssuchasBACnet4,Koinnex(KNX)5,LonWorks6,andModbus(Pricopetal., 2017).
• Actuators:Actuatorsrespondtotheoutputsignalsfromacontrollerandaccomplishesactionstooperate thefinalcontroldevice,whichmightbeavalve,damper,orswitch(S.Wang, 2009).Actuatorscontrol functionssuchasadjustingtemperature,operatingvalves,turningon/offlights,andmanagingsecurity systems.Similartosensors,actuatorsareeitherdirectlyconnectedtocontrollersorthroughafield network(Kastneretal., 2005).
• Human-MachineInterface(HMI):TheHMIistheuserinterfacethroughwhichbuildingoperatorsor occupantsinteractwiththeBAMS.Itcanbeagraphicalinterfacedisplayedonacomputer,touchpanels,or mobiledevices,allowinguserstomonitorsystemstatus,adjustsettings,andreceivealarmsornotifications. Generally,theHMIisdevelopedtomonitortheenergymanagementsystemregardingenergyconsumption (Hmidahetal., 2022).BAMS’SHMIisbuiltuponsupervisorycontrolanddataacquisitionplatformsto provideaunifiedvisualisationforallsystemsandsubsystemswhichfacilitatesthetaskoftheoperator (FigueiredoanddaCosta, 2012;Kastneretal., 2005).
• CommunicationInfrastructure:BAMScomponentsareinterconnectedthroughacommunicationnetwork, allowingdataexchangeandcoordination.Thisinfrastructuremayincludewiredorwirelessprotocols,such asEthernet,BACnet,LonWorks,Modbus,orotherindustry-specificprotocols.
• SupervisoryControlandDataAcquisition(SCADA):SCADAsystemsoffercentralisedmonitoringand controlcapabilitiesforlarge-scaleBAMSdeployments.Thesesystemsempoweroperatorsbyeliminating theneedtohandleeachpieceofequipmentlocallywithinabuildingorcomplex.Instead,theyenableremote monitoringandcontrol,allowingforthedetectionofabnormalconditionswithouttherequirementofbeing physicallyon-site.SCADAsystemsgatherdatafromvariouscontrollersandsensors,providingadvanced analytics,datafusion,andreportingfunctionalitiestoenhanceoperationalefficiencyanddecision-making (NesaandBanerjee, 2017;Kastneretal., 2005).
4Anobject-orientedpeer-to-peernetworkprotocol
5KNX,isapopularopenstandardforhomeandbuildingautomation.Twistedpair(TP),powerline(PL),radiofrequency(RF),and Ethernet(IP)areamongthein-housecommunicationpossibilitiescoveredbyKNX(Shikhlietal., 2022)
6Astandardisedbussystemusedincentralisedanddecentralisedbuildingautomationcontrol(Merz,Hansemann,andHübner, n.d.)
• EnergyManagementSystem(EMS):AnEMScomponentwithintheBAMSfocusesonoptimisingenergy consumptionandefficiency.Itanalysesenergydata,identifiesareasforimprovement,andimplements strategiestoreduceenergyusageandcosts.
• IntegrationGateways:Integrationgatewaysplayacrucialroleinensuringseamlessinteroperabilitybetween variousbuildingsystemsandprotocols.Thesegatewaysserveascommunicationbridges,enablingthe exchangeofdataandinformationbetweentheBAMSandsubsystemslikeHVAC,lighting,security,lifesafety, andaccesscontrol.Byadoptingthegatewayapproach,controlapplicationsondifferentnetworkscan utilisetheirnativeprotocolstocommunicatewithoneanother,whilethegatewaytakescareofestablishing thesemanticconnection.Thisapproachallowsfortheabstractionandconcealmentofthecomplexities associatedwithspecificprotocols,astheyarehandledbehindthescenesbythegateway.Asaresult, integrationgatewaysfacilitatesmoothandefficientcommunicationbetweendiversebuildingsystems, promotinginteroperabilityandstreamlinedfunctionality(Kastneretal., 2005).
• DatabasesandDataStorage:BAMSsystemsoftenincludedatabasestostorehistoricaldata,eventlogs,and configurationinformation.Thesedatabasessupportreporting,trendanalysis,andlong-termperformance monitoring.
• AlarmManagementandNotification:TheBAMSincludesmechanismstodetectandrespondtoabnormal conditionsorfaults.Itcangeneratealarmsandnotifications,alertingbuildingoperatorsormaintenance personnelofpotentialissuesrequiringattention.
Methodology Itmaybepossibletouseabuilding’sthermalmasstoachievedemandflexibility,ifitisknowninadvancewhen theflexibilityisrequired.Themethodologyweusetorealisethisflexibilityisacombinationofmachinelearning andoptimisationtechnology,toultimatelyderiveacontrolset-pointschedulethatreduces(orincreases,as required)theenergyconsumptionofthebuildingduringtheflexibilitywindow.
Figure 3.1 showsthehigh-leveloverviewofthecomponentsthatmakeupthe(ideal)methodology.Inthedata collectionphase,sensorreadingsarecollectedfromthebuilding’ssensorsandstoredinalong-termstorage database.Subsequently,themachinelearningphaseconsistsoffittingapredictivemodeltothereadingsinthe database.Thepredictivemodeltakesthetheexternal(uncontrollable)conditionstogetherwiththecontrol signalasinputfeatures,andpredictsanexttemperature(oratemperaturetrajectory).Thispredictivemodel isusedintheoperationalphasebyanoptimisationmodelthattakesasinputthecurrentstateoftheexternal conditionstogetherwiththepredictivemodel,andproducescontrolset-points(oracontrolset-pointschedule) tobedeployedonthebuilding.
Datacollection phase
Machinelearning phase
Operational phase
Timestamp,T_in,T_out,T_set,T_fcast,kW 2023-08-01,20.3,8.2,21.0,[8.5,9.0,…],2.0 2023-08-02,20.5,8.6,21.0,[9.0,9.2…],0.0
<now>,21.5,11.0,21.0,[…],2.5
Machinelearning
Surrogatemodel ofBuildingHVAC
<now>,21.5,11.0,21.0,[…],2.5
Database Evaluation
ComparisonoftCO2,$, kWh,DRcompliance
Using
BAMSControl BuildingHVAC
Forecastsofweather, occupancy;set-point schedule
Sensors Act
Actualweather, occupancy
Optimise
Optimisation
Forecastsof CO2,wholesale, weather,occupancy; set-pointschedule,DR
Sensors Act Act
BAMSControl BuildingHVAC
Sensors
Actualweather,occupancy, CO2,wholesale
Figure3.1: Idealisedmachine-learningandoptimisationpipelinetotransitionfroma‘normal’buildingtoa‘flexible’building. AnexistingbuildingwithBAMSisinstrumentedtocollectoperationaldata(datacollectionphase).Asurrogatemodelisfitto thisdatatomatchthebuilding’sthermalresponseunderdifferentweatherandoccupancymodes(machinelearningphase). Intheoperationalphase,thereal-timeBAMSset-pointscheduleisoptimisedusingthesurrogatemodelandadditional forecastinformationsuchasthecarbonintensityandwholesaleelectricityprice(blueinputs).
Thismethodologyhasbeenshowntoworkusingsimulatedbuildingmodels(Lametal., 2020),demonstrating optimalflexibleloadresponsescalculatedforthecontrolofupto20buildings.However,thepracticalapplication ofthismethodologyintherealworldfacessignificanthurdles.Crucially,thequalityoftheoptimisedcontrol scheduledependsinlargepartontheaccuracyandcompletenessoftheinputdatausedtofitthebuildingmodel. However,traditionallybuildingoperatorshavehadlittleneedtokeepaccuratehistoricsensorlogsacrosslong horizons,resultinginlongleadtimesnecessarytocollectenoughreal-worlddatatomaketheapplicationfeasible.
Becauseofthelargecostinvolvedinmaintainingahigh-accuracydatacollectionframework(includingthe maintenanceandmonitoringofsensorsfordrift),animportantquestiontoanswerishowmuchdataisneededto makethismethodologywork.Thisresearchaimstoanswerthisquestionviaasimulationapproach:by simulating thedatacollectionphaseatdifferentlevelsofaccuracyandgranularity,wecanobtaina(best-case)estimationof theamountofdatanecessarytoreconstructa(sufficiently)accuratepredictivemodel.
3.1 Buildingsimulationsoftwareanddata 3.1.1 EnergyPlus
EnergyPlusistheofficialbuildingenergysimulationprogramfromtheU.S.DepartmentofEnergy.Itmodels heating,cooling,lighting,ventilation,waterusage,andotherenergyflowswithinbuildings.Akeystrengthisits abilitytoperformintegratedsub-hourlysimulationsofinterconnectedheattransferpathsacrosstheentire building.EnergyPlusaccuratelyaccountsforwindowgains,shading,daylighting,renewables,andconstruction materials.Itsdetailedmodellingallowstheoptimisationofenergy-efficientbuildingandHVACdesignswhile consideringwateruseandutilitycosts.Therobustprogramiswidelyusedbytheglobalbuildingenergyanalysis community(Fumo,Mago,andLuck, 2010).
ThemethodologyproposedinthisstudyispredicatedondataderivedfromEnergyPlussimulationsofHVAC controlandbuildingthermalresponse,withaparticularemphasisonthesetpointsrepresentingthethermal zonesofthetargetbuilding.Incontrasttocollectingreal-timebuildingcontroldatafromBAMS,EnergyPlus simulationmodelsofferanefficientmeansofstreamliningthedatacollectionandinterpretationprocesses.For intricatecommercialbuildings,thesubstantialvolumeandcomplexinterconnectionsofHVACsystems,electrical equipment,andothercomponentsnecessitateextensivedatasciencecapabilitiesforcleaning,interpretation,and analysis.EnergyPlusenergysimulationmodelsconsolidatethedataintoaunifiedformatatacontrollablescalefor subsequentdeep-learningmodeldevelopmentwhilecomparativelyreflectingthesalientphysicalpropertiesofthe buildingunderinvestigation,includingsize,internalandexternalmaterials,andbasicHVACsystemconfiguration.
Nonetheless,asatrade-offbetweenresourceallocation,time,andaccuracy,thisstudyconsiderstheintegration ofEnergyPlussimulationmodelsforAI-drivencontrolasareasonablemethodologyforachievingflexiblebuilding goals.
3.1.2 OpenStudio OpenStudioisacross-platformsuiteofsoftwaretoolsdevelopedbytheNationalRenewableEnergyLaboratory (NREL)tofacilitatewhole-buildingenergymodellingusingtheEnergyPlussimulationengine.Akeycomponent istheOpenStudioApplicationSuite,whichprovidesauserinterfaceandgeometryeditorforcreating,editing, running,andvisualisingEnergyPlusmodels.
GiventheinherentcomplexityofconstructingathermalmodeldirectlywithintheEnergyPlusenvironment,this studyutilisedOpenStudiotostreamlinetheprocessofbuildingthefoundational3DmodelviaSketchUpSoftware andconfiguringtheHVACsystemforsubsequentEnergyPlussimulations.ByleveragingOpenStudio’sintegrated
toolset,thedifficultiesassociatedwithestablishinganEnergyPlusmodelfromtheoutsetweremitigated,enabling amoreefficientworkflow.
Ultimately,OpenStudioservesasacomprehensivesoftwareframeworkdesignedtofacilitateandexpeditebuilding energymodellingworkflowsbyprovidingauser-friendlyinterfaceanddevelopmentenvironmentforharnessing therobustsimulationcapabilitiesofEnergyPlus.Asanopen-sourcetool,itbenefitsfromanactivedeveloper communitycontributingtoplugins,enhancements,andtechnicalsupport.
3.1.3 Climatedata Inordertosimulatearangeofdifferentpotentialweatherconditions,EnergyPlusneedsaccesstoaweatherdata filethatrepresentsthetypicalmeteorologicalyear.WemakeuseoftheexistingdatasetofEnergyPlusweather datafilesforAustraliaproducedbyCSIRO(Ren,Tang,andJames, 2021).Thisdatasetisbasedonhistorical weatherdatadrawnfromtheyears1990to2015,containinghourlyobservationsforanentireyear.Sinceweare modellingabuildinglocatedinClayton,Victoria,theclosestdatafileis‘MelbourneRO’,whichistheonethatwe usedinoursimulations.
Amajorchallengeinperformingvalidatedstudiesintooptimisedbuildingcontrolisthefactthatvirtuallyno datasetforclimateandweatherdatacontainsforecastdatainadditiontothetruesignal.Withoutaccessto forecastdata,studiesmusteitherassumeperfectforecastsorattempttoproducereasonablyrealisticforecast errors.Unfortunately,duetothecomplexityinweatherprediction,successfullycreatingrealisticforecasterrors isunlikely.Becauseofthis,forourstudywewillalsorestrictourselvestoperfectforecasts,whichwillresultin over-estimationofthetruebestperformance,butrepresentsthebest possible outcome.
3.2 Simulatingamulti-purposeuniversitycampusbuilding FortestingthemethodologyproposedinFigure 3.1 wedevelopedanEnergyPlusmodelofamulti-purposecampus buildinghousingbothofficeworkers,lecturehallsandbreakoutspacesforcollaborativework.Wepatternedour buildingmodelontherecentlydelivered‘WoodsideBuildingforTechnologyandDesign’onMonash’Clayton campus.Thisbuildinghasseveralpropertiesthatmakeitparticularlysuitableforthisstudy:ithasmultiple thermalzonesacrossseveralopenandconfinedspaces,ithousesbothresearchlabsandstudentlecturehalls, andithasahighthermalcapacitanceowingtoitsPassivhausdesign(Grimshaw, 2020).
3.2.1 EnergyPlusmodeldesignparametersandfloorplan
TheprojectemployedSketchupandOpenStudioforconstructingtheEnergyPlusmodel,utilisingthereal-world geometricdimensions(120x42x25metres)andfundamentalmaterialdata.CompliancewithAIRAHStandard 189.1-2009,theBuildingCodeofAustralia(NCCVolume1),AustralianStandard1668.2,andtheAIRAHTechnical Handbookwasmaintainedtoestablishparametersforboththeexteriorandinteriorbuildingmaterials,thetype ofbuilding,defaultelectricalequipment,occupancy,andpredefinedschedulesforeachthermalzone.
Forthisproject,threeEnergyPlussimulationmodelsweredeveloped,eachprogressivelyincreasingthenumber ofthermalzonestomoreaccuratelyrepresentthecomplexityofthethermaldynamicsandreal-timeHVAC controloperations.Takingthe35thermalzonesmodelasanexample,thebuildingissegmentedintofivefloors. Eachfloorispartitionedintoaprimaryspaceflankedbysixsmaller,interconnectedspaces,asshowninFigure 3.2
Whilethetruearchitecturalcomplexityofthereal-worldbuildingismoreintricate,thisprojectemploysthe building’stotalannualenergyconsumptiondatatoensuretheapproximationmatchesintermsofthesimulated energyconsumption,thenumberofthermalzones,HVACconfigurations,andbuildingmaterialconfiguration. Themodelthusservesasacloserepresentationoftherealbuilding’sthermalbehaviourandenergydynamics.
Figure3.2: MostgranularversionoftheEnergyPlussimulationmodel,showingthesubdivisionofeachfloorinto7zones. Onelargeopenspace(left)and6equally-sizedzones(right),ofwhichonezoneisentirelyintheinterior.
3.2.2 SimulatingtheHVACSystem TheHVACsystemwithinthebuildingsimulationincorporatesacomprehensiveclimatecontrolsolutiontailored forbothefficiencyandcomfort.Thesystemfeaturespackagedrooftopairconditioners,designedforeaseof installationandmaintenance,providingreliableandcentralisedcoolingthroughoutthebuilding.Additionally,the simulationincludesavariableairvolume(VAV)systemwithreheatcapabilities,ensuringprecisetemperature controlacrossdifferentzonesandtheabilitytoadjustairflowbasedonoccupancyandthermaldemand,thereby reducingenergyconsumption.Servicehotwatersystemsareintegratedintothedesigntomeetthebuilding’s hotwaterneedsforvarioususessuchasrestroomsandkitchenfacilities.Thismulti-facetedapproachtoHVAC ensuresthatthebuildingmaintainsoptimalindoorairqualityandcomfortwhilestrivingforenergyefficiency.
TostreamlinethethermalsimulationprocesswithintheEnergyPlusmodelandplacegreateremphasisonthe applicationofdeeplearningandoptimisationmethodsforHVACcontrol,thisprojectconsolidatesallthebuilding’s thermalzonesintoaunifiedHVACsystemasdetailedinFigure 3.3
3.2.3 SimulatingtheOccupancySchedule Anaccuraterepresentationofoccupancypatternsiscrucialforpredictingbuildingenergyconsumptionin EnergyPlussimulations.Occupancydirectlyimpactsheating/coolingloadsthroughheatgainsfromoccupantsas wellasequipmentandlightingusageschedulestiedtooccupancy.
Universitybuildingsoftenexhibithighlyvariableandintricateoccupancyschedules,necessitatingtheconsideration ofcyclicacademiccalendars,seasonalvariations,weekdayandweekenddifferences,andotherpertinentfactors.
ToaccuratelycapturethesecomplexitieswithintheEnergyPlusmodellingframework,thisstudyemployeda stochasticapproachtogenerateoccupancydataprofilesgroundedinexistingspatial-temporaloccupancypattern studies(Juetal., 2023).
Specifically,theoccupancymodellingprocessaccountedforthedynamicnatureofuniversityfacilitiesbyincorporatingthefollowingelements:cyclicacademiccalendarstoreflecthigheroccupancyduringinstructional periodsandloweroccupancyduringbreaks,seasonalfluctuationstocapturevariationsinoccupancypatterns
Figure3.3: TheEnergyPluscomponentsusedinthesimulatedbuilding’sHVACsystem.
acrossdifferenttimesoftheyear,anddistinctionsbetweenweekdayandweekendoccupancylevelstoaccount forthediverseactivitiesandschedulesinherenttouniversitycampuses.
Byintegratingthesefactors,thegeneratedoccupancydataaimedtoprovideacomprehensiveandrealistic representationofthespatio-temporaldynamicsgoverningoccupancywithinthecontextofauniversitysetting. ThisapproachfacilitatedamoreaccurateassessmentoftheenergyconsumptionprofilesandHVACsystem requirementswithintheEnergyPlussimulations,therebyenhancingthereliabilityandapplicabilityofthemodelling outcomes.
3.3 Optimisationalgorithmsforflexiblebuildingcontrol Optimisationreferstotheprocessoffindinganassignmentofvaluestodecisionvariables x thatmaximises(or minimises)theobjectivefunction f (x),possiblysubjecttosomeconstraintsonthekindofvaluesthat x are allowedtotake.Informallystated,theoptimisationproblemforflexiblebuildingcontrolcanbestatedasfollows: minimise energyconsumptionduringflexibilityrequest subjectto occupantcomfortconstraints plantoperationalconstraints
Inthismodelsketch,occupantcomfortconstraintsrelatetomaintainingcomfortableindoortemperatureand humidityinoccupiedzonesofthebuilding,whileplantoperationalconstraintsrefertoanyrestrictionsthatmight applytothecontrolsignal(forexample,toavoidexcessivewearontheHVACplantduetofrequentswitching).
Therearemultiplewaystoimplementsuchanoptimisationtask;thechoiceofapproachdependsontheruntime andaccuracyrequirements.Exhaustivesearchmethodscanfindasolutionthatisprovengloballyoptimal,atthe
(a) Packagedrooftopairconditioner
(b) PackagedrooftopVAVwithreheat
(c) ServiceHotWater
(3.1)
costofimposingsomemandatorystructureonthekindofconstraintsthatcanbeencoded,whiletrial-based searchmethodsareopen-endedinthekindsoffunctionstheycanoptimiseover,atthecostofbeingunableto provideaccuracyestimatesoroptimalityguarantees.Inthiswork,wefocusontheexhaustivesearchmethods, becausewewanttomeasuretheaccuracyofthepredictionmodels,ameasurewhichwouldbeaffectedby findingsub-optimalsolutionsduetotheoptimisationmethod.
WeusetheMiniZinclanguageandcompilertorapidlyprototypetheconstraintoptimisationprograms.MiniZinc isdevelopedatMonashasadomain-specificlanguagethatcantranscribeanoptimisationprogramspecifiedata highlevelofabstractionintoalow-levelformulationthatcanbeinterpretedbyoneofthemanycommercial andopen-sourcesolversavailable.AlthoughMiniZinccancompiletodifferentsolverparadigms(i.e.,Constraint Programming,Mixed-IntegerProgramming,andSatisfiabilitysolvers),wewillexplicitlytargetMixed-Integer Programmingsolversbecausewewillneedtodealwithfloating-pointquantities(i.e.,temperature)whichare generallynothandledefficientlyintheothersolvertypes.
SeveralrecentworkshaveexploredtheinclusionofneuralnetworksintoMixed-IntegerProgramming(MIP) models,withAndersonetal.(2020)givingamathematicaltreatmentofanefficientformalismtocapturethe operationsofaReLU-basedneuralnetworkinaMIP.
4 Simulationexperiments Computersimulationmodelsforheatingandcoolingloadshavebeenaroundforalongtime,withoneofthe earliestproposedbyMortensenandHaggerty(1988).Thissimplemodelmirrorsanelectriccircuitwithasingle resistance,capacitor,andacurrentsource.Figure 4.1 demonstratestheresponseofanexampleinstanceofsuch athermalmodel.Thethermalresponseisanexponentialdecayacrossthefullrangeoftemperature,however,it isapproximatelylinearinthethermalcomfortrangearoundtheusualset-point θset ≈ 21°C.Thiscomfortrange isannotatedbyadead-bandaroundtheset-pointrangingfrom θ∆ to θ∆+ .Temperaturesinsidethisdead-band rangeareassumedtobe‘sufficientlycomfortable’fortheoccupantofthezone(orotherwiseacceptableforthe thermaldemandsofthezone,incasethemodelisforadifferentkindofzonelikearefrigerator).
Figure4.1: Thermalresponsecurvesofthesimplestthermalsimulationmodelundercooling(‘off’)andheating(‘on’) operation,fordifferentexternalconditions.
Asimpleflip-flophysteresiscontrollerforsuchadead-bandsystemwillkeepthetemperaturewithintheindicated boundsatalltimes,whileallowingforsomemodicumofoperatorflexibility.Figure 4.2 (left)showsasimulationof anaggregateof200thermalloadswithsmallrandomvariationinthermalparameters,operatingunderhysteresis control.Thefirst10loadsareshowninlightgreydashedlines,demonstratinghowthedirectionoftemperature changeinvertsatthedead-bandedges.At t =400,theflexibilityofthearrayiscalledupon,bymanuallyoverriding allthehysteresiscontrollerstothe‘off’or‘cooling’setting,independentlyoftheircurrentmodeortemperature. Asaresult,theloaddemandoftheaggregateimmediatelydropsto0,however,itreboundsrelativelyquicklyas thedevicesreachthelowerdead-bandedge.
Bycontrast,Figure 4.2 (right)demonstratesanoptimalcoordinatedflexibilityresponse.Underthiscondition,the thermalzonesareoptimisedfor minimum energyusageatalltimes,butwithanadditionalconstrainttoensure thateverydeviceisatmaximum θ∆+ at t =400.Asaresultofthiscontroloperation,thedemandforpower rapidlyrampsuptomaximumatthetimeleadinguptothedemand-responseevent.However,oncetheevent hits,demandcanstayat0foroverhalfanhourwithoutbreachinganyofthedevices’comfortbounds.Notethat bothcontrolmechanismsguaranteethecomfortlimitsatalltimes.
Hypothesis1. Optimal(minimum)energyoperationofthermalzonesandoptimal(maximum)flexibilityoperationofthermalzonesare orthogonal concernsthatcan both betargetedatthesametime.
Figure4.2: Flexibilityresponseofanaggregateof200buildingsunderreactivecontrol(aone-timedeactivationofheatpumps),versusaplannedoptimaltrajectoryforthesametrajectory.
Hypothesis2. Controllingforoptimalflexibilityrequiresproactive,planningcontroloverafuturehorizon, involvingconcernsof forecasting andpredictive what-ifmodels
Thisoptimalcontrolmethodologycanequallybeappliedtominimisethecarbonintensityoftheenergyconsumed, ifaforecastofcarbonintensityisavailable.Figure 4.3 demonstratesthisprinciplewiththesameaggregateof thermalloads,butnowoptimisingthenumberofdeviceson(pt,i =1),timesthecarbonintensity Ct attime t, acrosstheentireplanninghorizon h
Suchanoptimisationmodelisreadilysolvableifthetransferfunctionofzone i, fi canbeencodedlinearly (e.g.,multi-linearregressionmodel,orneuralnetworkwith ReLU activationfunctions).Inthecaseofthese simplethermalmodelswhichcanbeencoded directly,optimisingsuchamodeltakeslessthanasecondon amoderndesktopPC,foracontrolhorizonseveralhoursout.AsTable 4.1 shows,comparedtooptimising foronlyenergyconsumption( h t=1 n i=1 pt,i),optimisingforcarbonintensityreducesthecarbonimpactby approximately 11%,atthecostof 2 4%morepower(forthisparticularinstance,onaper-unitbasisbecausethe powerconsumptionoftheheatersisnotdimensioned).
(4.1)
Proportion heating / Carbon intensity (blue)
Figure4.3: Optimalflexibilityresponseofanaggregateofthermalloadsagainstacarbonintensitysignal.
Table4.1: Effectofoptimisingforminimumgridcarbonemissionsversusminimumbuildingenergyconsumption.
Solutionmetric:
Objective:Totalcarbon(p.u.)Totalenergy(p.u.)
Minimisecarbon 2637 43840 6
Minimiseenergy 2966 53753 9
5 Discussion—ChallengesandOpportunities Buildingshaveapotentialroletoplayinourfutureelectricitygrid,ifasufficientvolumeofHVACloadcan beconvertedintoflexibledemand.Thisprojectinvestigatedanddemonstratedthesoftwareanddatasideof themethodologyrequiredtoenableAI-readyflexiblebuildings.Nevertheless,manychallengesremainbefore widespreadreal-worldadoptionofthetechnologyispossible:
• Noteverybuildingissuitableforthismethodology.Onlybuildingswithsufficientthermalmassandsensor instrumentationaresuitablecandidates.
Recommendation: Buildingenergyefficiencycomesbeforeenergyflexibility.Existingbuildingstock shouldbeimprovedandupgraded,includingenhancingtheirsensorcoverage,andlong-termdatacollection shouldbeestablishedwherepossible.
• Applicationofthismethodologyrequiresadeliberatededicationofbuildingmanagerstoup-frontdata collection.However,itmayonlybecost-effectivetoapplythismethodologyonceabuildingisbeing refurbishedforenergyefficiency.Afterrefurbishing,itsthermaldynamicswillhavechanged,andhistoric datawillnotberelevantanymore.
Recommendation: Futureworkshouldlookintoalgorithmstosafelytransfersimulation-trainedthermal modelstoreal-worldbuildings,togetherwithrapidmodeladaptionmechanisms.
• Currentmethodologyassumesbespokethermalmodelsandoptimisationformulationswillbecreatedfor eachbuilding.Thesemustbealignedtotheavailablesensordataandavailableactuators.Wideadoption ofthismethodologycanonlybeachievedifitcanbesimplifiedintoaproduct.
Recommendation: Futureworkshouldlookintoautomaticmodelextractionfromschematics,for examplebymakinguseofbuildingontologytomapconceptsinBAMStothemethodology.
Byfittingamachinelearningmodelofabuildingthatcapturesenoughofthethermaldynamics,wecanapply optimisationalgorithmstoimprovetheloadprofileofabuildingtotargetacontrolsignalsuchasgridcarbon intensity.Wehavedemonstratedthismethodologyonahigh-accuracysimulationmodelrunninginEnergyPlus, provingtheconceptispossible.Withsufficientforecastinformationandhighthermalmodelaccuracy,thecarbon intensityofthebuilding’sheatingandcoolingrequirementsmaybereducedbyupto11%.However,real-world validationexperimentsarenecessarytovalidatethefindingsofthisstudyundermorerealisticconditionssuchas uncertaintyovertheforecastdataandsensordrift,tounderstandhowmuchoftheidealcarbonreductioncan beachievedinpractice.
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