Using Artificial Intelligence Methods for Power Estimation in Photovoltaic Panels

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dc.authoridumut, ilhan/0000-0002-5269-1128
dc.authorwosidumut, ilhan/A-2772-2017
dc.contributor.authorAkal, Dincer
dc.contributor.authorUmut, Ilhan
dc.date.accessioned2024-06-12T11:12:22Z
dc.date.available2024-06-12T11:12:22Z
dc.date.issued2022
dc.departmentTrakya Üniversitesien_US
dc.description.abstractThe limited reserves of fossil resources, the fluctuations in their prices and the damage they cause to the environment have led countries to seek alternatives to primary energy resources. Solar energy, which is an unlimited and environmentally friendly resource, is a powerful alternative to other energy sources. The majority of the European Union countries offer various opportunities to consumers in electricity generation from solar energy with many incentive mechanisms and ensure their widespread use. In many parts of the world, interest in renewable energy sources such as solar, wind, hydrogen and geothermal is also growing. In addition to all these, researches are continuing to use alternative energy sources and to make energy production more efficient. The radiation value required to obtain electricity from solar energy varies according to the weather conditions during the day and seasonal characteristics. The climatic conditions in the area where solar power plants are installed directly affect the output power and energy cost to be obtained from photovoltaic panels. Estimating the output power produced from photovoltaic panels according to environmental conditions, guiding companies in the installation of solar energy systems, obtaining maximum energy, energy management and efficient operation of the system are of great importance. In this study, feedforward back propagation artificial neural networks and KNN (K-Nearest Neighbors) methods were used to estimate power values using the data (Temperature, Humidity, Pressure, Radiation) obtained from the installed photovoltaic panels. Thus, the panel values obtained under real field conditions were trained with both methods and the results were compared. As a result, the power values of the panel were classified using the artificial neural network model developed with the highest accuracy of 98.7945%. It has been seen that the machine learning models used for solar energy estimation developed within the scope of this study have high performance and can produce results very close to the real values. In addition, it was concluded that both artificial intelligence models developed in locations with different characteristics according to the determined load demand can be used.en_US
dc.identifier.doi10.33462/jotaf.1023838
dc.identifier.endpage445en_US
dc.identifier.issn1302-7050
dc.identifier.issn2146-5894
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85131382137en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage435en_US
dc.identifier.trdizinid1152665en_US
dc.identifier.urihttps://doi.org/10.33462/jotaf.1023838
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1152665
dc.identifier.urihttps://hdl.handle.net/20.500.14551/23146
dc.identifier.volume19en_US
dc.identifier.wosWOS:000813467900018en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isotren_US
dc.publisherUniv Namik Kemalen_US
dc.relation.ispartofJournal Of Tekirdag Agriculture Faculty-Tekirdag Ziraat Fakultesi Dergisien_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectPhotovoltaic Panelen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectEfficiencyen_US
dc.subjectEnergyen_US
dc.subjectPoweren_US
dc.subjectPredictionen_US
dc.titleUsing Artificial Intelligence Methods for Power Estimation in Photovoltaic Panelsen_US
dc.typeArticleen_US

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