基于LSTM和誤差修正的光伏發(fā)電短期功率預(yù)測

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關(guān)鍵詞：光伏發(fā)電；誤差修正；優(yōu)化算法；經(jīng)驗?zāi)B(tài)分解；功率預(yù)測

DOI：10.15938/j. jhust.2025.02.013

中圖分類號：TM723 文獻標(biāo)志碼：A 文章編號：1007-2683（2025）02-0122-09

Abstract：Inordertoimprovethestabilityof photovoltaicpowergridconnectionandmakefulluseoferorinformationtocorect themodelpredictionresults，thispaperproposesashort-temphotovoltaicpowerpredictionmodelbasedonlongshor-temmemory （LSTM）anderorcorrection.First，thedataispreliminarilypredictedbyLSTMtogenerateanerrorsequence，andthentheerror sequenceisdecomposedintosubmodelsofdiferentfrequenciesbyempiricalmodedecomposition（EMD）.Similaritymeasureentis conductedaccording to Hausdorffdistance（HD），andeach modalcomponent isasigned weights，and thenLSTMoptimizedby SparowSearch Algorithm（SSA）isusedtopredicterormodalcomponents.The weighted predictionerror iscombinedwith the predictedvaluetoceverorcorrtionTouhexperiments，ithaseenproventatteodelproposdintisarticletpefos traditionalLSTMmodels，BP models，and SVMmodels inevaluation indicatorssuch asrotmeansquareerror（RMSE）and mean absolute percentage error （MAPE），verifying the effectiveness of the combined model.

Keywords：photovoltaicpower generation；errorcorrection；optimization algorithm；empirical modedecomposition；powerpredic tion

0 引言

在化石燃料短缺和氣候急劇變化的背景下，世界各地正在逐步推進新能源的開發(fā)和利用[1-2]。（剩余13092字）