基于ICEEMDAN-PE-GDBO-LSSVM的風(fēng)電功率預(yù)測

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中圖分類號：TN911.7-34；TM614 文獻(xiàn)標(biāo)識碼：A 文章編號：1004-373X（2025）10-0057-06

Abstract：Withthehighpenetrationofrenewableenergy，especiallywind power，thepowergrid isfacingunprecedented chalengesofuncertaintyandvolatity.Inordertoacuratelypredictwindpower，acombinedmodelbasedonimproved completeensemble empirical mode decompostionwith adaptivenoise （ICEEMDAN）-permutation entropy（PE）-improved dung beetle optimizationalgorithm（GDBO）-least support squaresvector machine （LSSVM） isproposed.ICEEMDANis used to decompose thewindpowerdata toreducethecomplexity.Thecomponentsobtainedafterdecompositionareagregatedaccording toPE，andthenthekeyparametersofLSSVMareoptimizedbymeansofGDBOalgorithm toobtain thebestpredictionmodel. Theoptimizationmodelisusedtopredictandsuperimposetheaggregationcomponents toobtain the totalpredictionresult.The experimentalverification isconductedbasedonthedomesticwindfarmdataset.Ttheresultsshowthattheproposed method has high predictionaccuracyandtheroot meansquare errris 61.39%lowerthan thatofthesingle LSSVMmodel，whichhasa broader application prospect in engineering practice.

Keywords：wind power prediction; ICEEMDAN; GDBO; PE;improved complete ensemble empirical mode decomposition; LSSVM; component polymerization

0 引言

隨著社會對環(huán)境保護(hù)和可持續(xù)發(fā)展的重視程度不斷提升，清潔能源產(chǎn)業(yè)正經(jīng)歷前所未有的蓬勃發(fā)展。（剩余6339字）