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基于小世界优化的支持向量机风电功率预测.docx

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基于小世界优化的支持向量机风电功率预测 Abstract Inrecentyears,windpowerhasbecomeanimportantsourceofrenewableenergy,andaccuratewindpowerpredictioncangreatlyimprovethemanagementandcontrolofwindfarms.Amongvariouswindpowerpredictionmethods,SupportVectorMachine(SVM)hasreceivedsignificantattentionduetoitsexcellentperformance.However,thestandardSVMmodelhaslimitationsinhandlingnon-linearandhigh-dimensionaldata.Therefore,inthispaper,weproposeanoptimizedSVMmodelbasedonthesmallworldnetworktheorytoenhancetheperformanceofwindpowerprediction.TheproposedmodeliscomparedwiththestandardSVMmodel,andtheresultsshowthattheoptimizedSVMmodelcaneffectivelyimprovetheaccuracyofwindpowerprediction. Introduction Windpowerhasbeenrecognizedasanimportantalternativesourceofrenewableenergyduetoitsenvironmentalbenefitsandeconomicadvantages.Accuratewindpowerpredictionisessentialforthemanagementandcontrolofwindfarms,asitcanhelptoimprovetheintegrationofwindpowerintothepowergridandreducethecostsofpowergeneration.However,windpowerpredictionisachallengingtaskduetothecomplexandnon-linearnatureofwindpowergeneration. SupportVectorMachine(SVM)isapopularmachinelearningmethodforwindpowerpredictionduetoitsrobustnessandaccuracy.However,thestandardSVMmodelhaslimitationsinhandlingnon-linearandhigh-dimensionaldata.Inrecentyears,manystudieshaveproposedoptimizationmethodstoenhancetheperformanceofSVMforwindpowerprediction.Inthispaper,weproposeanoptimizedSVMmodelbasedonthesmallworldnetworktheory. Smallworldnetworktheoryisamathematicalmodelthatdescribesthecomplexnetworksfoundinmanynaturalandsocialsystems.ThesmallworldmodelwasfirstproposedbyWattsandStrogatzin1998,andithasbeenwidelyappliedinvariousfields,suchasbiology,sociology,andcomputerscience.Thesmallworldnetworkischaracterizedbyhighclusteringandshortpathlengths,whichmeansthatithasbothlocalandglobalconnectivity.Thesmallworldnetworktheoryhasbeenusedinmachinelearningtoimprovetheperformanceofclassificationandregressiontasks. Inthispaper,weapplythesmallworldnetworktheorytooptimizetheSVMmodelf