基于CEEMD和GWO的超短期风速预测

基于CEEMD和GWO的超短期风速预测Title:Short-TermWindSpeedForecastingusingCEEMDandGWOAbstract:Inrecentyears,theaccurateforecastingofwindspeedhasbecomeincreasinglyimportantduetothegrowingdemandforrenewableenergysources.Windspeedforecastingplaysavitalroleintheefficientoperationandmanagementofwindpowergenerationsystems.Inthispaper,ahybridapproachcombiningtheCompleteEnsembleEmpiricalModeDecomposition(CEEMD)andGreyWolfOptimization(GWO)algorithmsisproposedtoforecastshort-termwindspeed.TheCEEMDtechniqueisusedtodecomposethewindspeedtimeseriesintoseveralintrinsicmodefunctions(IMFs)whichcapturethecomplextemporaldynamicsofwindspeed.Meanwhile,theGWOalgorithmisemployedtooptimizetheparametersoftheforecastingmodel,enhancingtheaccuracyofthewindspeedpredictions.Theproposedapproachisevaluatedusingrealwindspeeddata,andtheresultsdemonstrateitssuperiorityintermsofaccuracyandeffectivenesscomparedtotraditionalforecastingmethods.1.IntroductionTheintermittentandnon-stationarynatureofwindspeedposeschallengesforaccurateforecasting.Conventionalforecastingmethods,suchasstatisticalmodelsandartificialneuralnetworks,havelimitationsincapturingthecomplexdynamicsofwindspeed.Toovercometheselimitations,thispaperproposesahybridapproachthatcombinestwoadvancedtechniques:CEEMDandGWO.TheCEEMDtechniqueenablesthedecompositionofwindspeedtimeseriesintoIMFs,whichcapturedifferenttemporalscalesofthedata.TheGWOalgorithmoptimizestheparametersoftheforecastingmodeltoimprovepredictionaccuracy.2.Methodology2.1CompleteEnsembleEmpiricalModeDecomposition(CEEMD)CEEMDisanempiricalmodedecompositiontechniquethatovercomesthelimitationsoftraditionalFourierorwaveletdecompositions.ItdecomposesatimeseriesintomultipleIMFswithdifferenttemporalscales.Thedecompositioniscompletedthroughaniterativeprocessthatextractsthehigh-frequencycomponentsandtrendsfromtheoriginaldata.2.2GreyWolfOptimization(GWO)GWOisametaheuristicalgorithminspiredbythehuntingbehaviorofgreywolves.Itiscommonlyusedforoptimizationproblems.Inthisstudy,GWOisemployedtooptimizetheparametersoftheforecastingmodel,includingthenumberofdecompositionlevels,theIMFcomponentstobeselected,andthelagvaluesusedforforecasting.3.ExperimentalSetup3.1DataCollectionRealwindspeeddatafromawindfarmiscollectedforevaluationpurposes.Thedataincludeshistoricalwindspeedmeasurementstakenatregularintervals.3.2ModelImplementationThewindspeedtimeseriesisdecomposedintoIMFsusingCEEMD.Then,aforecastingmodelisconstructedusingtheselectedIMFsandtheoptimizedparametersthroughtheGWOalgorithm.Themodelistrainedusingaportionofthedataandevaluatedusingtheremainingportion.4.ResultsandAnalysisTheproposedhybridapproachiscomparedwithtraditionalstatisticalmodelsandartificialneuralnetworks.TheperformanceofthemodelsisevaluatedbasedonstatisticalmetricssuchasMeanAbsoluteError(MAE),RootMeanSquareError(RMSE),andMeanAbsolutePercentageError(MAPE).TheresultsdemonstratethattheCEEMD-GWOapproachoutperformstheothermethodsintermsofpredictionaccuracyandeffectiveness.5.ConclusionThispaperproposesanovelapproachforshort-termwindspeedforecastingbycombiningthestrengthsofCEEMDandGWO.TheCEEMDtechniqueeffectivelycapturesthecomplextemporaldynamicsofwindspeed,whiletheGWOalgorithmoptimizestheforecastingmodelparameters.Theexperimentalresultsconfirmthesuperiorityoftheproposedapproachovertraditionalforecastingmethods.Thefindingsofthisstudycanbevaluableforwindpowergenerationsystems,contributingtooptimaloperationandmanagement.Furtherresearchcanfocusonextendingthisapproachtomulti-step-aheadforecastingandincorporatingotherrelevantfactorssuchasweatherconditionsandterraincharacteristics.References:[Insertrelevantreferences]Note:Theprovidedcontentisageneraloutlineforapaperon