Mohamed, Z. (2019). Using the artificial neural networks for prediction and validating solar radiation. test, 27(1), 1-13. doi: 10.1186/s42787-019-0043-8
Zahraa E. Mohamed. "Using the artificial neural networks for prediction and validating solar radiation". test, 27, 1, 2019, 1-13. doi: 10.1186/s42787-019-0043-8
Mohamed, Z. (2019). 'Using the artificial neural networks for prediction and validating solar radiation', test, 27(1), pp. 1-13. doi: 10.1186/s42787-019-0043-8
Mohamed, Z. Using the artificial neural networks for prediction and validating solar radiation. test, 2019; 27(1): 1-13. doi: 10.1186/s42787-019-0043-8

Using the artificial neural networks for prediction and validating solar radiation

Journal of the Egyptian Mathematical Society
Volume 27, Issue 1, 2019, Pages 1-13    PDF (1.17 M)
DOI: 10.1186/s42787-019-0043-8
Author
Zahraa E. Mohamed
Department of Mathematics, Faculty of Science, Zagazig University, P.O. Box 44519, Zagazig, Egypt
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.

Keywords
Artificial neural network; Backpropagation algorithm; Solar radiation; Egypt
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