Application of extreme learning machine for estimating solar radiation from satellite data

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dc.authoridsahin, mehmet/0000-0001-7942-9253
dc.authoridUYAR, Murat/0000-0001-7243-7939
dc.contributor.authorSahin, Mehmet
dc.contributor.authorKaya, Yilmaz
dc.contributor.authorUyar, Murat
dc.contributor.authorYildirim, Selcuk
dc.date.accessioned2024-12-24T19:24:08Z
dc.date.available2024-12-24T19:24:08Z
dc.date.issued2014
dc.departmentSiirt Üniversitesi
dc.description.abstractIn this paper, a simple and fast method based on extreme learning machine (ELM) for the estimation of solar radiation in Turkey was presented. To design the ELM model, satellite data of the National Oceanic and Atmospheric Administration advanced very high-resolution radiometer from 20 locations spread over Turkey were used. The satellite-based land surface temperature, altitude, latitude, longitude, month, and city were applied as input to the ELM, and the output variable is the solar radiation. To show the applicability of the ELM model, a performance comparison in terms of the estimation capability and the learning speed was made between the ELM model and conventional artificial neural network (ANN) model with backpropagation. The comparison results showed that the ELM model gave better estimation than the ANN model for the overall test locations. Moreover, the ELM model was about 23.5 times faster than the ANN model. The method could be used by researchers or scientists to design high-efficiency solar devices such as solar power plant and photovoltaic cell. Copyright (c) 2013 John Wiley & Sons, Ltd.
dc.identifier.doi10.1002/er.3030
dc.identifier.endpage212
dc.identifier.issn0363-907X
dc.identifier.issn1099-114X
dc.identifier.issue2
dc.identifier.scopus2-s2.0-84892552548
dc.identifier.scopusqualityQ1
dc.identifier.startpage205
dc.identifier.urihttps://doi.org/10.1002/er.3030
dc.identifier.urihttps://hdl.handle.net/20.500.12604/5859
dc.identifier.volume38
dc.identifier.wosWOS:000329677500006
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley-Blackwell
dc.relation.ispartofInternational Journal of Energy Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectsolar radiation
dc.subjectextreme learning machine
dc.subjectremote sensing
dc.subjectsatellite data
dc.subjectNOAA
dc.titleApplication of extreme learning machine for estimating solar radiation from satellite data
dc.typeArticle

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