The artificial neural network model to estimate the photovoltaic modul efficiency for all regions of the Turkey

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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.authorGedik, Engin
dc.contributor.authorErkaymaz, Okan
dc.contributor.authorGurel, Ali Etem
dc.date.accessioned2024-09-29T15:55:18Z
dc.date.available2024-09-29T15:55:18Z
dc.date.issued2014
dc.departmentKarabük Üniversitesien_US
dc.description.abstractArtificial neural network (ANN) is a useful tool that using estimates behavior of the most of engineering applications. In the present study, ANN model has been used to estimate the temperature, efficiency and power of the Photovoltaic module according to outlet air temperature and solar radiation. An experimental system consisted photovoltaic module, heating and cooling sub systems, proportional integral derivative (PID) control unit was designed and built. Tests were realized at the outdoors for the constant ambient air temperatures of photovoltaic module. To preserve ambient air temperature at the determined constant values as 10, 20, 30 and 40 degrees C, cooling and heating subsystems which connected PID control unit were used in the test apparatus. Ambient air temperature, solar radiation, back surface of the photovoltaic module temperature was measured in the experiments. Obtained data were used to estimate the photovoltaic module temperature, efficiency and power with using ANN approach for all 7 region of the Turkey. The study dealing with this paper not only will beneficial for the limited region but also in all region of Turkey which will be thought established of photovoltaic panels by the manufacturer, researchers and etc. (C) 2014 Elsevier B.V. All rights reserved.en_US
dc.description.sponsorshipKarabuk University Scientific Research Projects Unit, Karabuk/TURKEY [KBO-BAP-13/2-YL-037]en_US
dc.description.sponsorshipThe authors would like to thank the Karabuk University Scientific Research Projects Unit, Karabuk/TURKEY for providing the financial supports for this study under the KBO-BAP-13/2-YL-037 project.en_US
dc.identifier.doi10.1016/j.enbuild.2014.08.003
dc.identifier.endpage267en_US
dc.identifier.issn0378-7788
dc.identifier.issn1872-6178
dc.identifier.scopus2-s2.0-84907499603en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage258en_US
dc.identifier.urihttps://doi.org/10.1016/j.enbuild.2014.08.003
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4578
dc.identifier.volume84en_US
dc.identifier.wosWOS:000345182000025en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Science Saen_US
dc.relation.ispartofEnergy and Buildingsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSolar energyen_US
dc.subjectPhotovoltaicen_US
dc.subjectArtificial neural networken_US
dc.titleThe artificial neural network model to estimate the photovoltaic modul efficiency for all regions of the Turkeyen_US
dc.typeArticleen_US

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