The prediction of photovoltaic module temperature with artificial neural networks

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dc.authoridERKAYMAZ, OKAN/0000-0002-1996-8623
dc.authoridGurel, Ali Etem/0000-0003-1430-8041
dc.contributor.authorCeylan, Ilhan
dc.contributor.authorErkaymaz, Okan
dc.contributor.authorGedik, Engin
dc.contributor.authorGurel, Ali Etem
dc.date.accessioned2024-09-29T15:55:15Z
dc.date.available2024-09-29T15:55:15Z
dc.date.issued2014
dc.departmentKarabük Üniversitesien_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 degrees 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, L4ak and Afyon are the most suitable cities in terms of electrical efficiency and power product in the Aegean region in Turkey. (C) 2014 The Authors. Published by Elsevier Ltd. Open access under CC BY licenseen_US
dc.identifier.doi10.1016/j.csite.2014.02.002
dc.identifier.endpage20en_US
dc.identifier.issn2214-157X
dc.identifier.startpage11en_US
dc.identifier.urihttps://doi.org/10.1016/j.csite.2014.02.002
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4548
dc.identifier.volume3en_US
dc.identifier.wosWOS:000216836500002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherElsevieren_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.subjectANNen_US
dc.subjectPhotovoltaicen_US
dc.subjectPoweren_US
dc.subjectElectrical efficiencyen_US
dc.titleThe prediction of photovoltaic module temperature with artificial neural networksen_US
dc.typeArticleen_US

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