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https://elibrary.tucl.edu.np/handle/123456789/8666Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Chalise, Sushant | - |
| dc.date.accessioned | 2022-03-02T08:25:53Z | - |
| dc.date.available | 2022-03-02T08:25:53Z | - |
| dc.date.issued | 2021-09 | - |
| dc.identifier.citation | MASTERS OF SCIENCE IN CLIMATE CHANGE AND SUSTAINABLE DEVELOPMENT PROGRAMME | en_US |
| dc.identifier.uri | https://elibrary.tucl.edu.np/handle/123456789/8666 | - |
| dc.description | Solar energy has immense promise as a source of renewable energy. Itisabundantthroughout theyear,althoughitissubjecttouncertaintyduetovariousparameters.Sunenergysources’ | en_US |
| dc.description.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. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Pulchowk Campus | en_US |
| dc.title | "Estimation of Global Solar Radiation Potential using Hybrid Models : A Case Study of Nepal” | en_US |
| dc.type | Thesis | en_US |
| local.institute.title | Institute of Engineering | en_US |
| local.academic.level | Masters | en_US |
| local.affiliatedinstitute.title | Pulchowk Campus | en_US |
| 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 |
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