基于非线性特征工程的短期建筑能耗预测方法.docx

基于非线性特征工程的短期建筑能耗预测方法
Title:Short-termBuildingEnergyConsumptionPredictionMethodBasedonNonlinearFeatureEngineering
Abstract:
Accuratepredictionofshort-termbuildingenergyconsumptioniscriticalforenergymanagementandoptimization.However,buildingenergyconsumptionpredictionisachallengingtaskduetothecomplexandnonlinearrelationshipsbetweenvariousfactorsthataffectenergyconsumption.Inthispaper,weproposeanewmethodforshort-termbuildingenergyconsumptionpredictionbasedonnonlinearfeatureengineering.Ourmethodleveragesbothmachinelearningtechniquesanddomainknowledgeinbuildingenergysystemstoextractandengineermeaningfulfeaturesthatcapturethenonlinearityandcomplexityofenergyconsumptionpatternsinbuildings.Specifically,wedevelopafeatureextractionframeworkthatintegratesvariousdatasources,includingbuildingthermodynamics,occupancy,weather,andenergyuse,intoasetofhigh-dimensionalfeaturevectors.Then,weapplyseveralmachinelearningmodels,includingsupportvectorregression,randomforest,andneuralnetwork,totrainandpredictbuildingenergyconsumptionbasedontheengineeredfeatures.Weevaluateourmethodusingreal-worlddatafromseveralbuildings,andcomparethepredictionaccuracyofdifferentmodels.Ourresultsshowthatourmethodsignificantlyoutperformstraditionalmethodsbasedonlinearfeatureengineeringandsimplestatisticalmodels,andachievesstate-of-the-artpredictionaccuracyforshort-termbuildingenergyconsumptionprediction.
Keywords:Buildingenergy,Featureengineering,Machinelearning,Nonlinearity,Prediction
1.Introduction
Buildingenergyconsumptionaccountsforasignificantportionofglobalenergyconsumption,andisamajorcontributortogreenhousegasemissionsandclimatechange.Accuratepredictionofbuildingenergyconsumptioniscriticalforeffectiveenergymanagementandoptimization,suchasdemand-response,energy-efficientcontrol,andrenewableenergyintegration.However,buildingenergyconsumptionpredictionisachallengingtaskduetothecomplexandnonlinearrelationshipsbetweenvariousfactorsthataffectenergyconsumption,suchasbuildingthermodynamics,occupancy,weather,ande