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Prediction of Photovoltaic Panels Output Performance Using Artificial Neural Network

Prediction of Photovoltaic Panels Output Performance Using Artificial Neural Network

Abdelouadoud Loukriz, Djamel Saigaa, Abdelhammid Kherbachi, Mustapha Koriker, Ahmed Bendib, Mahmoud Drif
Copyright: © 2022 |Volume: 11 |Issue: 2 |Pages: 19
ISSN: 2160-9500|EISSN: 2160-9543|EISBN13: 9781683182634|DOI: 10.4018/IJEOE.309417
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MLA

Loukriz, Abdelouadoud, et al. "Prediction of Photovoltaic Panels Output Performance Using Artificial Neural Network." IJEOE vol.11, no.2 2022: pp.1-19. http://doi.org/10.4018/IJEOE.309417

APA

Loukriz, A., Saigaa, D., Kherbachi, A., Koriker, M., Bendib, A., & Drif, M. (2022). Prediction of Photovoltaic Panels Output Performance Using Artificial Neural Network. International Journal of Energy Optimization and Engineering (IJEOE), 11(2), 1-19. http://doi.org/10.4018/IJEOE.309417

Chicago

Loukriz, Abdelouadoud, et al. "Prediction of Photovoltaic Panels Output Performance Using Artificial Neural Network," International Journal of Energy Optimization and Engineering (IJEOE) 11, no.2: 1-19. http://doi.org/10.4018/IJEOE.309417

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Abstract

To ensure the safe and stable operation of solar photovoltaic system-based power systems, it is essential to predict the PV module output performance under varying operating conditions. In this paper, the interest is to develop an accurate model of a PV module in order to predict its electrical characteristics. For this purpose, an artificial neural network (ANN) based on the backpropagation algorithm is proposed for the performance prediction of a photovoltaic module. In this modeling approach, the temperature and illumination are taken as inputs and the current of the mathematical model as output for the learning of the ANN-PV-Panel. Simulation results showing the performance of the ANN model in obtaining the electrical properties of the chosen PV panel, including I–V curves and P–V curves, in comparison with the mathematical model performance are presented and discussed. The given results show that the error of the maximum power is very small while the current error is about 10-8, which means that the obtained model is able to predict accurately the outputs of the PV panel.