EPJ Web Conf.
Volume 128, 2016Theoretical and Experimental Studies in Nuclear Applications and Technology (TESNAT 2016)
|Number of page(s)||4|
|Section||Artificial Neural Network|
|Published online||15 November 2016|
Estimation of monthly global solar radiation in the eastern Mediterranean region in Turkey by using artificial neural networks
1 Osmaniye Korkut Ata University, Faculty of Arts and Science, Department of Physics, Osmaniye, Turkey
2 The Faculty of Economics and Business Administration, Management Information Systems, Osmaniye, Turkey
Published online: 15 November 2016
In this study, an artificial neural network (ANN) model was used to estimate monthly average global solar radiation on a horizontal surface for selected 5 locations in Mediterranean region for period of 18 years (1993-2010). Meteorological and geographical data were taken from Turkish State Meteorological Service. The ANN architecture designed is a feed-forward back-propagation model with one-hidden layer containing 21 neurons with hyperbolic tangent sigmoid as the transfer function and one output layer utilized a linear transfer function (purelin). The training algorithm used in ANN model was the Levenberg Marquand back propagation algorith (trainlm). Results obtained from ANN model were compared with measured meteorological values by using statistical methods. A correlation coefficient of 97.97 (~98%) was obtained with root mean square error (RMSE) of 0.852 MJ/m2, mean square error (MSE) of 0.725 MJ/m2, mean absolute bias error (MABE) 10.659MJ/m2, and mean absolute percentage error (MAPE) of 4.8%. Results show good agreement between the estimated and measured values of global solar radiation. We suggest that the developed ANN model can be used to predict solar radiation another location and conditions.
© The Authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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