Modeling of photovoltaic array output current based on actual performance using artificial neural networks
Errors; Neural networks; Photovoltaic cells; Regression analysis; Generalized regression; Generalized regression neural networks; Mean absolute percentage error; Mean bias errors; Photovoltaic arrays; Photovoltaic modules; Prediction accuracy; Root mean square errors; Mean square error
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American Institute of Physics Inc.
2023
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my.uniten.dspace-222752023-05-29T13:59:58Z Modeling of photovoltaic array output current based on actual performance using artificial neural networks Ameen A.M. Pasupuleti J. Khatib T. 56602552200 11340187300 31767521400 Errors; Neural networks; Photovoltaic cells; Regression analysis; Generalized regression; Generalized regression neural networks; Mean absolute percentage error; Mean bias errors; Photovoltaic arrays; Photovoltaic modules; Prediction accuracy; Root mean square errors; Mean square error This paper presents prediction models for photovoltaic (PV) module's output current. The proposed models are based on empirical, statistical, and artificial neural networks. The adopted artificial neural networks are generalized regression, feed forward, and cascaded forward neural networks. The proposed models have two inputs, namely, solar radiation and ambient temperature, while system's output current is the output. Two years of experimental data for a 1.4 kWp PV system are utilized in this research. These data are recorded every 10 seconds in order to consider the uncertainty of system's output current. Three statistical values are used to evaluate the accuracy of the proposed models, namely, mean absolute percentage error, mean bias error, and root mean square error. A comparison between the proposed models in terms of prediction accuracy is conducted. The results show that the generalized regression neural network based model exceeds the other models. The mean absolute percentage error, root mean square error, and mean bias error of the generalized regression neural network model are 4.97%, 5.67%, and -1.17%, respectively. � 2015 AIP Publishing LLC. Final 2023-05-29T05:59:58Z 2023-05-29T05:59:58Z 2015 Article 10.1063/1.4931464 2-s2.0-84942770133 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84942770133&doi=10.1063%2f1.4931464&partnerID=40&md5=4b8d2db05622f6ae008b12d05556b775 https://irepository.uniten.edu.my/handle/123456789/22275 7 5 53107 American Institute of Physics Inc. Scopus |
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Errors; Neural networks; Photovoltaic cells; Regression analysis; Generalized regression; Generalized regression neural networks; Mean absolute percentage error; Mean bias errors; Photovoltaic arrays; Photovoltaic modules; Prediction accuracy; Root mean square errors; Mean square error |
| author2 |
56602552200 |
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56602552200 Ameen A.M. Pasupuleti J. Khatib T. |
| format |
Article |
| author |
Ameen A.M. Pasupuleti J. Khatib T. |
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Ameen A.M. Pasupuleti J. Khatib T. Modeling of photovoltaic array output current based on actual performance using artificial neural networks |
| author_sort |
Ameen A.M. |
| title |
Modeling of photovoltaic array output current based on actual performance using artificial neural networks |
| title_short |
Modeling of photovoltaic array output current based on actual performance using artificial neural networks |
| title_full |
Modeling of photovoltaic array output current based on actual performance using artificial neural networks |
| title_fullStr |
Modeling of photovoltaic array output current based on actual performance using artificial neural networks |
| title_full_unstemmed |
Modeling of photovoltaic array output current based on actual performance using artificial neural networks |
| title_sort |
modeling of photovoltaic array output current based on actual performance using artificial neural networks |
| publisher |
American Institute of Physics Inc. |
| publishDate |
2023 |
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1806427390946050048 |