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基于风险的自适应风电预测方法及误差评估的开题报告 Abstract Windpowergenerationisanimportantpartofrenewableenergy.Windpowerpredictionisofgreatimportancetoensurethestabilityandreliabilityofthepowersystem.Duetothecomplexanduncertainnatureofwind,windpowerpredictionisachallengingtask.Inthisproposal,arisk-basedadaptivewindpowerpredictionmethodanderrorevaluationisproposed.Theproposedmethodwillconsidertheinherentuncertaintiesandcomplexdynamicrelationshipsinwindpowergeneration.Themethodwillusemachinelearningalgorithmsanddata-drivenmodelstoimprovetheaccuracyofwindpowerprediction. Introduction Windpowerisbecominganincreasinglyimportantsourceofrenewableenergy.Windpowerpredictioniscriticaltothestableandreliableoperationofpowersystems.Windpowerpredictionisachallengingtaskduetotheinherentuncertaintiesandcomplexdynamicinteractionsinwindpowergeneration.Traditionalwindpowerpredictionmethodsusestatisticalmethodsandphysicalmodelstopredictwindpower.Thesemethodsarenotaccurateenoughtomeettherequirementsofpracticalapplications. Toaddressthisissue,arisk-basedadaptivewindpowerpredictionmethodisproposed.Thismethodwillleveragemachinelearningalgorithmstoimprovetheaccuracyofwindpowerprediction.Theproposedmethodwillconsidertheinherentuncertaintiesandcomplexdynamicrelationshipsinwindpowergeneration.Themethodwillusedata-drivenmodelsthatcanadapttochangingconditionsandprovideaccuratepredictions. LiteratureReview Windpowerpredictionhasbeenextensivelystudiedintheliterature.Therearetwomainapproaches:physicalmodel-basedmethodsanddata-drivenmethods. Physicalmodel-basedapproachesuseatmosphericdataandphysicalmodelstopredictwindpower.Thismethodisaccurate,butitrequiresextensivedataandknowledgeofwindphysics.Thesemethodsarenoteffectiveinpredictingshort-termwindpower. Data-drivenmethodsusemachinelearningalgorithmstopredictwindpower.Thismethodisbasedonhistoricaldataandcanadapttochangingconditions.However,thismethodcanleadtoinaccuratepredictionsduetothecomplexanduncertainnatureofwind. ResearchMethodology Inthisproposal,arisk-basedadaptive