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基于子集门限自回归模型的负荷短期预测(英文).docx

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基于子集门限自回归模型的负荷短期预测(英文) Title:LoadShort-termForecastingbasedonSubsetThresholdAutoregressiveModel Abstract: Loadforecastingplaysacrucialroleinpowersystemoperation,asitprovidesvaluableinsightsforefficientgridmanagement,resourceallocation,andplanning.Accurateshort-termloadforecastingisparticularlyimportantforensuringthestabilityandreliabilityofpowersystems.Inrecentyears,thesubsetthresholdautoregressive(STAR)modelhasgainedsignificantattentionduetoitsabilitytocapturenon-linearandnon-stationaryloadbehavior.Thispaperpresentsanin-depthanalysisoftheSTARmodelanditsapplicationinloadshort-termforecasting. 1.Introduction Theaccurateforecastingofelectricloadisessentialforpowersystemoperatorsandplanners.Loadforecastinghelpsinmakinginformeddecisionsregardingpowergenerationandtransmission,optimizingresourceallocation,maintaininggridstability,andfacilitatingdemand-sidemanagement.Thispaperfocusesonshort-termloadforecasting,whichtypicallypredictstheloadinthenextfewhourstoafewdaysahead. 2.LiteratureReview Thissectionreviewstheexistingliteratureonloadforecastingtechniques,includingtraditionalmodelsliketheautoregressiveintegratedmovingaverage(ARIMA)modelandartificialintelligence-basedmodelssuchasartificialneuralnetworks(ANNs)andsupportvectormachines(SVMs).Additionally,itdiscussesthelimitationsofthesemodelsincapturingnon-linearandnon-stationaryloadbehavior. 3.SubsetThresholdAutoregressiveModel Thesubsetthresholdautoregressive(STAR)modelisaflexibleandpowerfultoolforcapturingcomplexloadpatterns.Itcombinestheadvantagesofthresholdautoregressive(TAR)modelsandsubsetmodelingtechniques.TheSTARmodelincorporatesathresholdmechanismthatallowsdifferentregimesorstatestobeidentified,therebycapturingnon-linearrelationshipsintheloaddata.Thesubsetmodelingtechniqueallowsfortheautomaticselectionofrelevantvariables,ensuringtheinclusionofimportantpredictorsforaccurateforecasting. 4.LoadDataPreprocessing BeforeapplyingtheSTARmodel,loaddatapreprocessingisnecessary.Thisstepinvolvestechniquessuchasdatacleaning,outli