基于递归量化分析的COA-SVR短期风速混合预测模型.docx

基于递归量化分析的COA-SVR短期风速混合预测模型
摘要
在风电发电厂中，风速是影响电力输出的主要因素之一。因此，精确预测风速变化对于最大化风力发电厂的效益至关重要。本文基于递归量化分析，结合支持向量机回归模型，提出了一种COA-SVR短期风速混合预测模型。该模型采用COA算法优化支持向量机回归模型的参数，实验结果表明，该模型的预测准确率要高于传统的SVR模型，且具有较好的鲁棒性和泛化能力。
关键词：递归量化分析，支持向量机回归，COA算法，短期风速预测
Abstract
Inwindpowergenerationplants,windspeedisoneofthemainfactorsthataffectsthepoweroutput.Therefore,accuratepredictionofwindspeedchangesiscrucialformaximizingtheefficiencyofwindpowergenerationplants.Inthispaper,basedonrecursivequantizationanalysis,combinedwithsupportvectormachineregressionmodel,aCOA-SVRshort-termwindspeedhybridpredictionmodelisproposed.ThemodeloptimizestheparametersofsupportvectormachineregressionmodelbyCOAalgorithm.ExperimentalresultsshowthatthepredictionaccuracyofthemodelishigherthanthatofthetraditionalSVRmodel,andithasgoodrobustnessandgeneralizationability.
Keywords:RecursiveQuantizationAnalysis,SupportVectorMachineRegression,COAAlgorithm,Short-termWindSpeedPrediction
1.Introduction
Windpowerisapromisingcleanenergysource,andthedevelopmentofwindpowergenerationtechnologyhasreceivedincreasingattention.Inrecentyears,withthecontinuousdevelopmentofwindpowertechnology,windpowergenerationcapacityhasgraduallyincreased,andwindpowerhasbecomeoneofthemostimportantrenewableenergysources.Windspeedisakeyfactorinwindpowergeneration,andaccuratewindspeedforecastingisessentialformaximizingtheefficiencyofwindpowerplants.
Windspeedforecastingisacomplexandchallengingtaskduetothevariabilityandrandomnessofwindspeed.Traditionalstatisticalmethodsandartificialneuralnetwork-basedmethodshavebeenwidelyusedinwindspeedforecasting.However,thesemethodshavesomedisadvantagessuchaslowaccuracyandpoorgeneralizationability.
Inrecentyears,supportvectormachines(SVMs)havebeenwidelyusedinwindspeedforecastingduetotheirstronggeneralizationability.However,theparametersofSVMsneedtobeoptimizedbysomeoptimizationalgorithmstoensuretheiroptimalperformance.
Inthispaper,aCOA-SVRshort-termwindspeedhybridpredictionmodelisproposedbasedonrecursivequantizationanalysisandsuppor