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基于小波神经网络的电网负荷预测.docx

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基于小波神经网络的电网负荷预测 Introduction Electricitydemandforecastingisanimportantaspectofpowersystemplanningandoperation.Accurateandreliableforecastsofelectricitydemandcanhelppowersystemoperatorsmakeinformeddecisionsaboutthedispatchofelectricitygeneration,theschedulingofmaintenanceactivities,andthemanagementofsystemreliability.Therearemanydifferentmethodsthatcanbeusedtoforecastelectricitydemand,includingstatisticalmodels,machinelearningalgorithms,andphysics-basedmodels.Inrecentyears,therehasbeengrowinginterestintheuseofartificialneuralnetworks(ANNs)forelectricitydemandforecastingduetotheirabilitytomodelcomplexnonlinearrelationshipsbetweeninputandoutputvariables. OnetypeofANNsthathasreceivedparticularattentioninrecentyearsisthewaveletneuralnetwork(WNN).WNNscombinethepowerofANNswiththeabilitytoanalyzesignalsinboththetimeandfrequencydomainsprovidedbywavelettransforms.Inthispaper,wepresentaWNN-basedmethodforelectricitydemandforecastingthatcaneffectivelycapturethetime-varyingandnon-stationarycharacteristicsofthedemandsignal. Methodology Theproposedmethodconsistsoffourmaincomponents:datapreprocessing,featureextraction,modeltraining,andmodelevaluation.Thedetaileddescriptionofeachcomponentisasfollows: DataPreprocessing:Inthisstep,therawelectricitydemanddataispreprocessedtoremoveanyoutliers,missingvalues,andseasonaltrends.Thepreprocessingstepalsoinvolvesscalingandnormalizationofthedatatoensurethatallinputvariablesareonthesamescale. FeatureExtraction:Thewavelettransformisusedtodecomposethepreprocesseddemanddataintomultiplefrequencybands,eachrepresentingadifferentleveloftemporalandspectralresolution.Thedecomposedsignalisthentransformedintoasetoffeaturesthatrepresentthedominantfrequencycomponentsofthesignalateachresolutionlevel. ModelTraining:TheWNNistrainedontheextractedfeaturesusingabackpropagationalgorithm.TheWNNconsistsofamultilayerfeedforwardnetworkwithwaveletactivationfunctionsineachlayer.Thetrainingalgorithmadjuststheweightsandbiasesofthenetworktominimizetheerrorbetweenthepredicted