Photovoltaic Panel Efficiency Estimation with Artificial Neural Networks: Samples of Adiyaman, Malatya, and Sanliurfa

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dc.authoridGursoy, Mehmet Ismail/0000-0002-2285-5160
dc.authoridGURSOY, Mehmet Ismail/0000-0002-2285-5160
dc.authoridMAMIS, MEHMET SALIH/0000-0002-6562-0839
dc.authorwosidGursoy, Mehmet Ismail/AAE-9130-2019
dc.authorwosidGURSOY, Mehmet Ismail/AAB-5751-2022
dc.authorwosidMamis, Mehmet/AAC-3247-2019
dc.contributor.authorIcel, Yasin
dc.contributor.authorMamis, Mehmet Salih
dc.contributor.authorBugutekin, Abdulcelil
dc.contributor.authorGursoy, Mehmet Ismail
dc.date.accessioned2024-08-04T20:46:43Z
dc.date.available2024-08-04T20:46:43Z
dc.date.issued2019
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe amount of electric energy produced by photovoltaic panels depends on air temperature, humidity rate, wind velocity, photovoltaic module temperature, and particularly solar radiation. Being aware of the behaviour patterns of the panels to be used in project and planning works regarding photovoltaic applications will set forth a realistic expense form; therefore, erroneous investments will be avoided, and the country budget will benefit from added value. The power ratings obtained from the photovoltaic panels and the environmental factors were measured and recorded for a year by the measurement stations established in three diverse regions (Adiyaman-Malatya-Sanliurfa). In the developed artificial neural network models, the estimation accuracy was 99.94%. Furthermore, by taking the data of the General Directorate of Meteorology as a reference, models of artificial neural networks were developed using the data from Adiyaman province for training; by using Malatya and Sanliurfa as test data, 99.57% estimation accuracy was achieved. With the artificial neural network models developed as a result of the study, the energy efficiency for the photovoltaic energy systems desired to be established by using meteorological parameters such as temperature, humidity, wind, and solar radiation of various regions anywhere in the world can be estimated with high accuracy.en_US
dc.description.sponsorshipScientific Research Project Unit of Adiyaman University [MUFMAP/2015-0011]en_US
dc.description.sponsorshipThis research has been supported by the Scientific Research Project Unit of Adiyaman University with the project numbered MUFMAP/2015-0011.en_US
dc.identifier.doi10.1155/2019/6289021
dc.identifier.issn1110-662X
dc.identifier.issn1687-529X
dc.identifier.scopus2-s2.0-85070107522en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1155/2019/6289021
dc.identifier.urihttps://hdl.handle.net/11616/98898
dc.identifier.volume2019en_US
dc.identifier.wosWOS:000465282100001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherHindawi Ltden_US
dc.relation.ispartofInternational Journal of Photoenergyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subject[No Keywords]en_US
dc.titlePhotovoltaic Panel Efficiency Estimation with Artificial Neural Networks: Samples of Adiyaman, Malatya, and Sanliurfaen_US
dc.typeArticleen_US

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