Abstract—This paper explores the possibility of developing a
prediction model using artificial neural networks (ANNs),
which could be used to estimate monthly average daily global
solar radiation in Qena, upper Egypt. Results from the paper
have shown good agreement between the estimated and
measured values of global solar irradiation. A correlation
coefficient of 0.998 was obtained with mean bias error (MBE) of
48 Wh/m2 and root mean square error (RMBE) of 115 Wh/m2.
The comparison between the ANN and empirical model
emphasized the superiority of the proposed ANN prediction
model. The application of the proposed ANN model can be
extended to other locations with similar climate and terrain.
Index Terms—Artificial neural network, global solar
radiation, sunshine duration.
The authors are with Physics Department, Faculty of Sciences, South
Valley University, Qena, Egypt (e-mail: ealy21@yahoo.com;
el_nouby.adam_svu@yahoo.com).
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Cite:Emad A. Ahmed and M. El-Nouby Adam, "Estimate of Global Solar Radiation by Using Artificial
Neural Network in Qena, Upper Egypt," Journal of Clean Energy Technologies vol. 1, no. 2, pp. 148-150, 2013.
