Department of Mathematics, Faculty of Science, Zagazig University, P.O. Box 44519, Zagazig, Egypt
https://doi.org/10.1186/s42787-019-0043-8
Abstract
The main objective of this paper is to employ the artificial neural network (ANN) models for validating and predicting global solar radiation (GSR) on a horizontal surface of three Egyptian cities. The feedforward backpropagation ANNs are utilized based on two algorithms which are the basic backpropagation (Bp) and the Bp with momentum and learning rate coefficients respectively. The statistical indicators are used to investigate the performance of ANN models. According to these indicators, the results of the second algorithm are better than the other. Also, model (6) in this method has the lowest RMSE values for all cities in this study. The study indicated that the second method is the most suitable for predicting GSR on a horizontal surface of all cities in this work. Moreover, ANN-based model is an efficient method which has higher precision.
Mohamed, Z. (2019). Using the artificial neural networks for prediction and validating solar radiation. Journal of the Egyptian Mathematical Society, 27(1), 1-13. doi: https://doi.org/10.1186/s42787-019-0043-8
MLA
Zahraa E. Mohamed. "Using the artificial neural networks for prediction and validating solar radiation", Journal of the Egyptian Mathematical Society, 27, 1, 2019, 1-13. doi: https://doi.org/10.1186/s42787-019-0043-8
HARVARD
Mohamed, Z. (2019). 'Using the artificial neural networks for prediction and validating solar radiation', Journal of the Egyptian Mathematical Society, 27(1), pp. 1-13. doi: https://doi.org/10.1186/s42787-019-0043-8
VANCOUVER
Mohamed, Z. Using the artificial neural networks for prediction and validating solar radiation. Journal of the Egyptian Mathematical Society, 2019; 27(1): 1-13. doi: https://doi.org/10.1186/s42787-019-0043-8
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