Applied Sciences and Chemical Engineering

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https://hdl.handle.net/20.500.14540/111

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Browsing Applied Sciences and Chemical Engineering by Author "Chalise, Sushant"

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    "Estimation of Global Solar Radiation Potential using Hybrid Models : A Case Study of Nepal”
    (Pulchowk Campus, 2021-09) Chalise, Sushant
    Solarenergyhasimmensepromiseasasourceofrenewableenergy.Itisabundantthroughout theyear,althoughitissubjecttouncertaintyduetovariousparameters.Sunenergysources’ affectivityandproductivitycanbeimprovedbyaccurateforecastingofsolarradiation.Fore- castingGlobalSolarRadiation(GSR)inthefieldofresearchhasattractedwidespreadattention fromtheresearchcommunityinmanypracticalfieldsincludingenergy.Differentmodelsfor predictingGSRpotentialhavebeenusedintheliterature.Oneofthemostprominentlinear modelsfortimeseriesforecastingistheAutoregressiveIntegratedMovingAverage(ARIMA). Therearealsodifferentmachinelearningmodelswhichshowpromisingforecastingresults.To takeadvantageoftheuniquebenefitsofARIMAandmachinelearningmodelsinlinearand nonlinearmodelingthedataofsolarradiationpotential,weproposeahybridmethodcombining ARIMAandmachinelearningmodelsANN(ArtificialNeuralNetwork)andLSTM(LongShort TermMemory)modelsinthisstudy.ThedatasetwasobtainedforthelocationofKushma, Parbatfordurationbetween1990to2014.Forthesupplieddatasets,theARIMAplusANN hybridmodelwasseentobethebestmethodforpredictingsolarradiationpotential.Thecor- relationcoefficient(Rsquare)iscalculated0.847.Theerrorvaluesforthismodelareaccessed asRMSE,MAPEandMAEof1.719,6.456and1.330respectively.Theexperimentalresults ofrealdatasetsshowthatthecombinedmodelcaneffectivelyimprovethepredictionaccuracy achievedbyanymodelusedalone.TTheacquiredresultsalsodemonstratedthatthecreated modelcouldbeutilizedtocalculatethesolarradiationpotentialofanygeographicregionwith knownclimaticparameters.