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1、IEEETRANSACTIONSONPOWERSYSTEMS,VOL.19,NO.4,NOVEMBER20041867ImprovingMarketClearingPricePredictionbyUsingaCommitteeMachineofNeuralNetworksJau-JiaGuo,StudentMember,IEEE,andPeterB.Luh,Fellow,IEEEAbstract—Predictingmarketclearingpricesisanimportantbutdifficulttask,andneuralnetworkshavebeenw
2、idelyused.Asingleneuralnetwork,however,maymisrepresentpartoftheinput-outputdatamappingthatcouldhavebeencorrectlyrepre-sentedbydifferentnetworks.Theuseofa“committeemachine”composedofmultiplenetworkscaninprinciplealleviatesuchadifficulty.Amajorchallengeforusingacommitteemachineistoproperl
3、ycombinepredictionsfrommultiplenetworks,sincetheperformanceofindividualnetworksisinputdependentduetomappingmisrepresentation.ThispaperpresentsanewmethodinwhichweightingcoefficientsforcombiningnetworkpredictionsFig.1.Schematicofanensemble-averagingcommitteemachinewithnetworkaretheprobabi
4、litiesthatindividualnetworkscapturethetruepredictionsy^andweightingcoefficientsa.input-outputrelationshipatthatpredictioninstant.TestingoftheNewEnglandmarketcleaningpricesdemonstratesthatthenewspaceofmixturesofexpertsisdividedintoseveralregionsmethodperformsbetterthanindividualnetworks,
5、andbetterthancommitteemachinesusingcurrentensemble-averagingmethods.(subspaces)towhichdifferentneuralnetworksareassigned.Agatingnetworkdecideswhichnetworkpredictionwillbese-IndexTerms—Committeemachines,energypriceforecasting,lectedbasedoninputdata[6].Incontrast,thesecondapproachmultipl
6、emodelapproach,neuralnetworks.combinespredictionsofmultiplenetworks.Awell-knownmethodistheensemble-averagingmethodasdepictedinFig.1I.INTRODUCTIONwherepredictionsofneuralnetworksarelinearlycombinedEURALNETWORKShavebeenwidelyusedinmanybasedonastraightaverageorthestatisticsofhistoricalpre
7、dic-Nforecastingproblems,includingloadandmarketclearingtionerrors[7][9].price(MCP)predictionsforpowersystems[1][3].ThemainTheneuralnetworksinFig.1maybeofdifferentkinds,orofreasonfortheirsuccessisthattheyarecapableofinferringthesamekindbutwithdifferentconfigurations(e.g.,differenthidde