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1、1266IEEETRANSACTIONSONPOWERSYSTEMS,VOL.25,NO.3,AUGUST2010IntelligentHybridWaveletModelsforShort-TermLoadForecastingAjayShekharPandey,DevenderSingh,andSunilKumarSinhaAbstract—Awaveletdecompositionbasedloadforecastap-Theelectricalloadatanyparticulartimeisusuallyassumedproachisproposedfor24-ha
2、nd168-haheadshort-termloadtobealinearcombinationofdifferentcomponents.Fromtheforecasting.Theproposedapproachisappliedtoandcomparedsignalanalysispointofview,loadcanalsobeconsideredasawithrepresentativeloadforecastingmethodssuchas:timese-linearcombinationofdifferentfrequencies.Thewavelettrans
3、-riesintraditionalapproachesandRBFneuralnetworkandformisintroducedtopreprocesstheloaddatainordertoen-neuro-fuzzyforecasterinnontraditionalapproaches.Theotherforecasters,suchasmultiplelinearregression(MLR),timeseries,hancetheaccuracyofforecasting.Inthiswork,inadditiontofeedforwardneuralnetwo
4、rk(FFNN),radialbasisfunctionneuralthehistoricalelectricitydemanddatathetemperature,whichhasnetwork(RBFNN),clustering,andfuzzyinferenceneuralnetworksignificantimpactonenergyconsumption,havealsobeenused.(FINN),reportedintheliteraturearealsocomparedwiththeTheloaddataaretransformedinlowandhighfr
5、equencycom-presentapproach.Theprocessoftheproposedwaveletdecompo-ponents.Itisshownthatthehighfrequencycomponentdoesnotsitionapproachisthatitfirstdecomposesthehistoricalloadandchangefromareferencedaytotheforecastdaywhereasthelowweathervariablesintoanapproximatepartassociatedwithlowfrequencies
6、andseveraldetailpartsassociatedwithhighfrequen-frequencycomponentissituation(climate)dependant.Henceaciescomponentsthroughthewavelettransform.Thehistoricalforecastmodelisdevelopedforthelowfrequencycomponentdataaresmoothenedbydeletingthehighfrequencycomponentsonly.andfedasinputtotheproposedm
7、odelsfortheprediction.Acom-AvarietyoftechniquesforSTLFbasedonconventionalsta-parisonofwaveletandnon-waveletbasedapproachesshowsthetisticalmethods,ANNbasedmodels,expertsystemsandhybridsuperiorityofproposedwaveletbasedapproachovernon-waveletmodelhavebeenpr