Photovoltaic Panel Efficiency Estimation with Artificial Neural Networks: Samples of Adiyaman, Malatya, and Sanliurfa
The 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 photovolta...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2019/6289021 |
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| _version_ | 1849412699055718400 |
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| author | Yasin Icel Mehmet Salih Mamis Abdulcelil Bugutekin Mehmet Ismail Gursoy |
| author_facet | Yasin Icel Mehmet Salih Mamis Abdulcelil Bugutekin Mehmet Ismail Gursoy |
| author_sort | Yasin Icel |
| collection | DOAJ |
| description | The 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. |
| format | Article |
| id | doaj-art-55451f58012b4ebd971cd1c1caaa8df0 |
| institution | Kabale University |
| issn | 1110-662X 1687-529X |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Photoenergy |
| spelling | doaj-art-55451f58012b4ebd971cd1c1caaa8df02025-08-20T03:34:21ZengWileyInternational Journal of Photoenergy1110-662X1687-529X2019-01-01201910.1155/2019/62890216289021Photovoltaic Panel Efficiency Estimation with Artificial Neural Networks: Samples of Adiyaman, Malatya, and SanliurfaYasin Icel0Mehmet Salih Mamis1Abdulcelil Bugutekin2Mehmet Ismail Gursoy3Electrical and Energy Department, Adiyaman University, Adiyaman, TurkeyElectrical and Electronics Engineering Department, Inonu University, Malatya, TurkeyMechanical Engineering Department, Adiyaman University, Adiyaman, TurkeyElectrical and Energy Department, Adiyaman University, Adiyaman, TurkeyThe 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.http://dx.doi.org/10.1155/2019/6289021 |
| spellingShingle | Yasin Icel Mehmet Salih Mamis Abdulcelil Bugutekin Mehmet Ismail Gursoy Photovoltaic Panel Efficiency Estimation with Artificial Neural Networks: Samples of Adiyaman, Malatya, and Sanliurfa International Journal of Photoenergy |
| title | Photovoltaic Panel Efficiency Estimation with Artificial Neural Networks: Samples of Adiyaman, Malatya, and Sanliurfa |
| title_full | Photovoltaic Panel Efficiency Estimation with Artificial Neural Networks: Samples of Adiyaman, Malatya, and Sanliurfa |
| title_fullStr | Photovoltaic Panel Efficiency Estimation with Artificial Neural Networks: Samples of Adiyaman, Malatya, and Sanliurfa |
| title_full_unstemmed | Photovoltaic Panel Efficiency Estimation with Artificial Neural Networks: Samples of Adiyaman, Malatya, and Sanliurfa |
| title_short | Photovoltaic Panel Efficiency Estimation with Artificial Neural Networks: Samples of Adiyaman, Malatya, and Sanliurfa |
| title_sort | photovoltaic panel efficiency estimation with artificial neural networks samples of adiyaman malatya and sanliurfa |
| url | http://dx.doi.org/10.1155/2019/6289021 |
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