预览加载中，请您耐心等待几秒...

基于改进增强型神经网络的短期电力负荷预测.docx

预览
1/3
2/3
3/3

在线预览结束，喜欢就下载吧，查找使用更方便

下载提示 文本预览

如果您无法下载资料，请参考说明：

1、部分资料下载需要金币，请确保您的账户上有足够的金币

2、已购买过的文档，再次下载不重复扣费

3、资料包下载后请先用软件解压，在使用对应软件打开

基于改进增强型神经网络的短期电力负荷预测 Abstract Electricityloadforecastingplaysacrucialroleinensuringtheefficientandreliableoperationofpowersystems.Accurateshort-termloadforecastingenablespowergridoperatorstomakeinformeddecisionsaboutelectricitygeneration,transmission,anddistribution.Inrecentyears,therehasbeengrowinginterestinusingartificialintelligencetechniques,suchasneuralnetworks,forloadforecasting.Thispaperproposesanimprovedenhancedneuralnetworkmodelforshort-termelectricityloadforecasting. Keywords:electricityloadforecasting,neuralnetworks,short-termforecasting 1.Introduction Withtheincreasingcomplexityanduncertaintyinelectricitymarkets,accurateloadforecastinghasbecomeasignificantconcernforpowersystemoperators.Electricityloadforecastinginvolvespredictingthefutureelectricityconsumptionbasedonhistoricalloaddataandvariousinfluentialfactorssuchasweatherconditions,holidays,andeconomicindicators.Reliableloadforecastingiscrucialforoptimizingpowersystemoperations,schedulinggeneration,andmaintaininggridstability. Neuralnetworkshaveshownpromisingresultsinloadforecastingduetotheirabilitytolearncomplexpatternsandrelationshipsfromhistoricaldata.However,traditionalneuralnetworkshavelimitationsincapturingthenonlinearanddynamicbehaviorofelectricityloadduetotheirstaticnature.Toaddressthisissue,animprovedenhancedneuralnetworkmodelisproposedinthispaper. 2.Methodology Theproposedenhancedneuralnetworkmodelconsistsofthreemaincomponents:datapreprocessing,networkarchitecture,andtrainingalgorithm. 2.1Datapreprocessing Inordertoimproveforecastaccuracy,itisessentialtopreprocesstheinputdata.Thisincludesremovingoutliers,handlingmissingvalues,andnormalizingthedata.Outliersinloaddatacanhaveasignificantimpactonforecastaccuracyandshouldbedetectedandremoved.Missingvaluescanbefilledusinginterpolationtechniquesorbyusinghistoricaldatafromthesametimeperiodtoestimatethemissingvalues.Normalizingthedatahelpsinachievingbetterconvergenceandgeneralizationoftheneuralnetworkmodel. 2.2Networkarchitecture Theneuralnetworkarch