基于特征选择和改进深度森林的短期风电功率预测-豆柴文库

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基于特征选择和改进深度森林的短期风电功率预测
Abstract:
Withtherapiddevelopmentofwindpowergeneration,accurateandreliableshort-termwindpowerforecastinghasbecomeincreasinglyimportantforthestableandeffectiveoperationofpowergrids.Inthispaper,ahybridmodelcombiningfeatureselectionandimproveddeepforestalgorithmisproposedforshort-termwindpowerforecasting.Throughtheanalysisofhistoricalwindpowergenerationdataandmeteorologicaldata,therelevantfeaturesareselectedtoimprovetheaccuracyandefficiencyofwindpowerforecasting.Moreover,theimproveddeepforestalgorithmisusedtoconstructthepredictionmodel,whichcaneffectivelyhandlethenon-linearanddynamiccharacteristicsofwindpowergeneration.Theexperimentresultsshowthattheproposedmodelcanachievehigheraccuracyandbetterrobustnessinwindpowerforecastingcomparedtothetraditionalforecastingmodels.
Introduction:
Asacleanandrenewableenergysource,windpowerhasbeenwidelyusedaroundtheworldforthepastdecade.However,duetotheuniquecharacteristicsofwindpower,suchasitsintermittency,randomnessanduncertainty,theaccurateandreliablepredictionofwindpoweroutputisstillachallengingtask.Theshort-termwindpowerforecastingisofgreatsignificanceinensuringthestabilityandsecurityofpowergridsaswellastheintegrationofwindenergyintothepowersystem.
Manystudieshavebeenconductedontheshort-termwindpowerforecasting,andvariousmodelsandalgorithmshavebeenproposed.Amongthem,themachinelearningalgorithms,suchasartificialneuralnetworks(ANN),supportvectormachines(SVM),andrandomforest(RF),havebeenwidelyusedduetotheirexcellentnon-linearfittingabilityandhighpredictionaccuracy.However,theconventionalmachinelearningmodelsmaysufferfromtheissuesofoverfitting,dimensionalitycurse,andredundantfeatures.Therefore,thispaperproposesahybridmodelbasedonfeatureselectionandimproveddeepforestalgorithmforwindpowerforecasting.
Methodology:
1.DataPreprocessingandFeatureSelection
Thedatapreprocessinginvolvesdatacleaning,normalization,andencoding.Thehistoricaldataofwindpowergenerationsandmeteorologicalfactorsareusedfortheanalysisandtrainingoft