基于GA-LSSVM与ARIMA组合的短期风电功率预测-豆柴文库

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基于GA-LSSVM与ARIMA组合的短期风电功率预测
Title:Short-termWindPowerForecastingusingGA-LSSVMandARIMAHybridModel
Abstract:
Windpowerforecastingplaysavitalroleintheefficientoperationandmanagementofwindfarms.Accurateshort-termwindpowerforecastscanfacilitategridintegration,reduceoperationalcosts,andoptimizeenergyscheduling.TheaimofthisstudyistoproposeahybridmodelcombiningGeneticAlgorithm(GA)optimizedLeastSquaresSupportVectorMachine(LSSVM)andAutoregressiveIntegratedMovingAverage(ARIMA)forshort-termwindpowerforecasting.Theproposedmodeltakesadvantageofthestrengthsofbothapproachestoimprovetheaccuracyandreliabilityofwindpowerpredictions.Theperformanceofthehybridmodelisevaluatedusingreal-worldwindpowerdata,andtheresultsarecomparedwithindividualGA-LSSVMmodelsandARIMAmodels.
1.Introduction:
Windenergyhasgainedsignificantimportanceasacleanandrenewablesourceofpowergeneration.However,itsintermittentandvariablenatureposesachallengeforintegratingwindpowerintotheelectricalgrid.Accurateforecastingofwindpowerfluctuationsisessentialforefficientgridmanagement,energyscheduling,andeconomicoperationofwindpowerplants.Severalforecastingtechniqueshavebeendevelopedovertheyears,includingstatisticalmodels,machinelearningapproaches,andhybridmodels.ThispaperfocusesonthedevelopmentofahybridGA-LSSVMandARIMAmodelforshort-termwindpowerforecasting.
2.LiteratureReview:
Thissectionpresentsareviewoftheexistingliteratureonwindpowerforecastingtechniques.Variousstatisticalmodels,suchasARIMA,havebeenwidelyusedforcapturingthelineardependenciesinwindpowertimeseriesdata.Machinelearningalgorithms,includingSupportVectorMachines(SVM),ArtificialNeuralNetworks(ANN),andRandomForest(RF),havealsobeenappliedtowindpowerforecasting.Theshortcomingsofindividualmodelsmotivatetheexplorationofhybridforecastingmodels,suchascombiningSVMandARIMAorSVMwithGeneticAlgorithmsforoptimization.
3.Methodology:
TheproposedhybridmodelcombinesGA-LSSVMandARIMAtoenhancetheaccuracyofshort-termwindpowerforecasting.TheARIMAcomponentcapturesthelineardependenciesan