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1、InfluenceofTemperatureonShort-TermLoadForecastingUsingtheEPRI-ANNSTLF122331D.Paravan,A.Debs,C.Hansen,D.Becker,PeterHirsch,andR.Golobsuchfactorsastemperatureandotherclimaticconditions,Abstract—Closetrackingofsystemloadbysystemtimeandtypeofthedayeffect,seasoneffect,etc.A
2、sagenerationatalltimesisthebasicrequirementintheresult,theartificialneuralnetworktechnologyisveryoperationofanypowersystem.Inthenewenvironment,asuitableforbuildingagenericshort-termloadforecastingTransmissionSystemOperator(TSO)requiresaccuratemodel.short-termloadforeca
3、ststoassurereliablesystemoperation.TheadvantageofagenericstructureiseasyOnesuchforecastingtoolistheEPRIartificialneuralimplementationofthemodelindifferentconditions.networkshort-termloadforecaster(EPRI-ANNSTLF)whichHowever,adrawbackisthatnotallspecificsofagivenutilizes
4、asingletemperaturevariableforeach“region”tobeforecasted.Iftherearetwoormoreweatherstationsinsuchaenvironmentcanbeembraced.Onesuchvaryingpropertyisregionthenaweightedaveragetemperatureissubstitutedforoftenthenumberoftemperaturevariables,wherethesingletemperaturevariable
5、.Thispaperfocusesonthemeasurementsfrommultipleweatherstationsareavailable.developmentofapracticalmethodologyforselectingsuchBasedonconsiderableresearchitwasrecommendedthatweather-stationweightingfactors.Inthepaper,twomethodsonlyonetemperaturebeusedforinputintotheANNmod
6、elforcalculatingweather-stationweightingfactorsforregions[2,3,4],thetemperaturedatahavetobepre-processedandwithmultipleweatherstationdataarepresented.Thereplacedwithaaweightedaveragetemperature.influenceoftemperatureonloadismeasuredwithcorrelationInthispapertheinfluenc
7、eoftemperatureonshort-termcoefficientandbyusingmutualinformationtheory.loadforecastingisinvestigated.TwomethodsformeasuringWeightingfactorsforeachweatherstationarethenrelationshipanddependencybetweeninputandoutputcalculatedbymaximizingthesethetwocriteria,whichisdonebym
8、eansofevolutionaryoptimisationtechnique.Thevariableareused:(i)correlationcoefficientand(ii)mutualproposedmethodsareve
