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Prediction of Photovoltaic Panels Output Performance Using Artificial Neural Network
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Author(s): Abdelouadoud Loukriz (LMSE Laboratory, Biskra University, Algeria), Djamel Saigaa (M'sila University, Algeria), Abdelhammid Kherbachi (Renewable Energy Development Center, Algeria), Mustapha Koriker (M'sila University, Algeria), Ahmed Bendib (Blida 1 University, Algeria)and Mahmoud Drif (M'sila University, Algeria)
Copyright: 2022
Volume: 11
Issue: 2
Pages: 19
Source title:
International Journal of Energy Optimization and Engineering (IJEOE)
Editor(s)-in-Chief: Jose Marmolejo-Saucedo (National Autonomous University of Mexico), Gerhard-Wilhelm Weber (Poznań University of Technology, Poland)and Pandian Vasant (Ton Duc Thang University, Vietnam)
DOI: 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.
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