Please use this identifier to cite or link to this item:
https://elibrary.tucl.edu.np/handle/123456789/8666
Title: | "Estimation of Global Solar Radiation Potential using Hybrid Models : A Case Study of Nepal” |
Authors: | Chalise, Sushant |
Issue Date: | Sep-2021 |
Publisher: | Pulchowk Campus |
Institute Name: | Institute of Engineering |
Level: | Masters |
Citation: | MASTERS OF SCIENCE IN CLIMATE CHANGE AND SUSTAINABLE DEVELOPMENT PROGRAMME |
Abstract: | 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. |
Description: | Solar energy has immense promise as a source of renewable energy. Itisabundantthroughout theyear,althoughitissubjecttouncertaintyduetovariousparameters.Sunenergysources’ |
URI: | https://elibrary.tucl.edu.np/handle/123456789/8666 |
Appears in Collections: | Applied Sciences and Chemical Engineering |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Sushant Chalise.pdf | 1.19 MB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.