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基于TRNSYS与深度学习模型的近零能耗建筑系统能耗与负荷预测.docx

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基于TRNSYS与深度学习模型的近零能耗建筑系统能耗与负荷预测 Abstract Withtheincreasingdemandforenergy-efficientbuildings,thepredictionofenergyconsumptionandloadofbuildingshasbecomeacriticalissue.Inthispaper,ahybridmodelbasedonTRNSYSanddeeplearningisproposedtopredicttheenergyconsumptionandloadofnearlyzero-energybuildings(nZEBs).TheTRNSYSmodelisusedtosimulatethebuilding'senergyperformance,whilethedeeplearningmodelisappliedtooptimizethepredictionaccuracy.Theproposedmodelcanhelpbuildingmanagerstooptimizeenergymanagementstrategies,reduceenergyconsumption,andachievethegoalofzero-energybuildings. Introduction Nearlyzero-energybuildings(nZEBs)arebuildingsthathaveaveryhighenergyperformancelevelandcomplywiththeenergyrequirementsoftheEuropeanUnion'sEnergyPerformanceofBuildingsDirective.Thesebuildingsaredesignedtoconsumelessenergythantheygeneratethroughrenewablesources,suchassolarorwindpower.Withtheincreasingimportanceofenergyefficiencyinbuildings,therehasbeenagrowinginterestindevelopingmethodstopredicttheenergyconsumptionandloadofnZEBs.Accuratepredictionofenergyconsumptionandloadcanhelpbuildingmanagersoptimizeenergymanagementstrategies,reduceenergyconsumption,andimprovetheoverallperformanceofnZEBs. TRNSYS(TransientSystemSimulationTool)isawidelyusedsoftwaretoolforsimulatingtheenergyperformanceofbuildings.Itconsistsofacollectionofcomponentmodelsthatrepresentthevarioussubsystemsofabuilding,suchastheheating,cooling,andventilationsystems.WithTRNSYS,itispossibletosimulatetheenergyperformanceofbuildingsundervariousoperatingconditions. Deeplearningisasubfieldofmachinelearningthatusesartificialneuralnetworkstomodelcomplexrelationshipsbetweeninputandoutputdata.Deeplearninghasbeenwidelyusedforenergy-relatedapplications,suchasloadforecastingandenergyconsumptionprediction. Inthispaper,weproposeahybridmodelthatcombinesTRNSYSanddeeplearningtopredicttheenergyconsumptionandloadofnZEBs.TheTRNSYSmodelisusedtosimulatetheenergyperformanceofthebuilding,whilethedeeplearningmodelisappliedtooptimizethepredictionaccuracy. Methodo