基于K-SVD-OMP和KELM组合方法的短期光伏功率预测（英文）-豆柴文库

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基于K-SVD-OMP和KELM组合方法的短期光伏功率预测（英文）
Title:Short-termPhotovoltaicPowerPredictionBasedonK-SVD-OMPandKELMCombinationMethod
1.Introduction(approx.200words)
Theadvancementinphotovoltaic(PV)technologyhasledtotherapidgrowthofsolarenergyasamajorrenewableenergysource.AccurateandreliablepredictionofPVpoweroutputiscrucialformanagingandintegratingsolarenergyintothepowergrid.Short-termPVpowerpredictionplaysacrucialroleingridstability,energytrading,andoptimaloperationofpowersystems.ThispaperproposesanovelcombinationmethodofK-SVD-OMP(K-SingularValueDecomposition-OrthogonalMatchingPursuit)andKELM(KernelExtremeLearningMachine)forshort-termPVpowerprediction.
2.LiteratureReview(approx.300words)
Manytraditionalforecastingtechniques,suchasautoregressiveintegratedmovingaverage(ARIMA)andartificialneuralnetworks(ANN),havebeenwidelyusedforPVpowerprediction.However,thesetechniquesoftenfacechallengesindealingwithnon-linearity,high-dimensionaldata,andcomplexpatternspresentinPVpowertimeseries.Toovercomethesechallenges,recentstudieshavefocusedontheintegrationofadvancedmachinelearningalgorithmsandsignalprocessingtechniques.
OnepopulartechniqueistheK-SVD-OMP,whichcombinessparsecodingwithdictionarylearning.Ithasshownpromisingresultsinvarioussignalprocessingapplications.Anotheradvancedalgorithm,KELM,basedonextremelearningmachine(ELM),providesefficientandaccuratepredictionsbymappinginputdataintoahigh-dimensionalfeaturespace.
3.Methodology(approx.400words)
Theproposedmethodconsistsoftwomainsteps:trainingandprediction.Inthetrainingphase,theK-SVD-OMPalgorithmisappliedtodecomposethePVpowertimeseriesintoasparserepresentationusingalearneddictionary.TheK-SVD-OMPmethodoptimizesthedictionaryandobtainsasparsecoefficientmatrixforthetrainingdata.
Inthepredictionphase,thesparserepresentationobtainedfromthetrainingphaseisfedintotheKELMmodel.KELMmapsthesparserepresentationintoahigh-dimensionalfeaturespaceusingakernelfunction.Theregressionmodelisthentrainedonthemappeddatatopredicttheshort-termPVpoweroutput.
4.Experime