The prediction of photovoltaic module temperature with artificial neural networks

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dc.contributor.authorCeylan, İlhan
dc.contributor.authorErkaymaz, Okan
dc.contributor.authorGedik, Engin
dc.contributor.authorGürel, Ali Etem
dc.date.accessioned2020-04-30T13:33:47Z
dc.date.available2020-04-30T13:33:47Z
dc.date.issued2014
dc.departmentDÜ, Düzce Meslek Yüksekokulu, Elektrik ve Enerji Bölümüen_US
dc.description.abstractIn this study, photovoltaic module temperature has been predicted according to outlet air temperature and solar radiation. For this investigation, photovoltaic module temperatures have been determined in the experimental system for 10, 20, 30, and 40 °C ambient air temperature and different solar radiations. This experimental study was made in open air and solar radiation was measured and then this measured data was used for the training of ANN. Photovoltaic module temperatures have been predicted according to solar radiation and outside air temperature for the Aegean region in Turkey. Electrical efficiency and power was also calculated depending on the predicted module temperature. Kutahya, U§ak and Afyon are the most suitable cities in terms of electrical efficiency and power product in the Aegean region in Turkey.en_US
dc.identifier.doi10.1016/j.csite.2014.02.001en_US
dc.identifier.endpage20en_US
dc.identifier.issn2214157X
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage11en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.csite.2014.02.001
dc.identifier.urihttps://hdl.handle.net/20.500.12684/731
dc.identifier.volume3en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofCase Studies in Thermal Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectANN; Electrical efficiency; Photovoltaic poweren_US
dc.titleThe prediction of photovoltaic module temperature with artificial neural networksen_US
dc.typeArticleen_US

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