Machine Learning Method for Solar PV Output Power Prediction
Sharkawy, A., Ali, M., Mousa, H., Ali, A., Abdel-Jaber, G. (2022). Machine Learning Method for Solar PV Output Power Prediction. EKB Journal Management System, 3(2), 123-130. doi: 10.21608/svusrc.2022.157039.1066
Abdel-Nasser Sharkawy; Mustafa M. Ali; Hossam H. H. Mousa; Ahmed S. Ali; G. T. Abdel-Jaber. "Machine Learning Method for Solar PV Output Power Prediction". EKB Journal Management System, 3, 2, 2022, 123-130. doi: 10.21608/svusrc.2022.157039.1066
Sharkawy, A., Ali, M., Mousa, H., Ali, A., Abdel-Jaber, G. (2022). 'Machine Learning Method for Solar PV Output Power Prediction', EKB Journal Management System, 3(2), pp. 123-130. doi: 10.21608/svusrc.2022.157039.1066
Sharkawy, A., Ali, M., Mousa, H., Ali, A., Abdel-Jaber, G. Machine Learning Method for Solar PV Output Power Prediction. EKB Journal Management System, 2022; 3(2): 123-130. doi: 10.21608/svusrc.2022.157039.1066

Machine Learning Method for Solar PV Output Power Prediction

SVU-International Journal of Engineering Sciences and Applications
Article 12, Volume 3, Issue 2, December 2022, Page 123-130  XML PDF (1.21 MB)
Document Type: Original research articles
DOI: 10.21608/svusrc.2022.157039.1066
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Authors
Abdel-Nasser Sharkawy email orcid 1; Mustafa M. Ali2; Hossam H. H. Mousaorcid 3; Ahmed S. Ali4; G. T. Abdel-Jaber3
1Mechatronics Engineering, Mechanical Engineering Department, Faculty of Engineering, South Valley University, Qena 83523, Egypt
2Mechatronics Engineering, Department of Mechanical Engineering, South Valley University, Qena 83523, Egypt
3Department of Electrical Engineering, South Valley University, Qena 83523, Egypt
4Mechatronics Engineering, Department of Mechanical Engineering, Assiut University, Assiut, Egypt
Abstract
To deal with the challenges of the solar photovoltaic (PV) energy source due to the continuous variations of the climatic conditions such as temperature and solar radiation, output power prediction is one of the most important research trends nowadays. In this paper, a multilayer feedforward neural network (MLFFNN) is executed to foresee the power for a solar PV power station. The MLFFNN employs the temperature and radiation as the inputs and the power as the output. For training and testing the MLFFNN, data of 6 days are acquired from a real PV power station in Egypt. The first five days are employed to train the MLFFNN using Levenberg-Marquardt (LM) algorithm. While the data of the sixth day, are used to check the effectiveness and the generalization ability of the trained MLFFNN. The results prove that the trained MLFFNN is working very well and efficient to predict the PV output power correctly.
Keywords
Power Prediction; Multilayer Feedforward NN; solar PV; Levenberg-Marquardt Algorithm; MLFFNN Effectiveness
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