Table of Contents
Advances in Artificial Neural Systems
Volume 2011, Article ID 751908, 7 pages
http://dx.doi.org/10.1155/2011/751908
Research Article

Predicting Global Solar Radiation Using an Artificial Neural Network Single-Parameter Model

Department of Physics, Makerere University, P.O. Box 7062, Kampala, Uganda

Received 30 May 2011; Revised 28 August 2011; Accepted 5 September 2011

Academic Editor: Ozgur Kisi

Copyright © 2011 Karoro Angela et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We used five years of global solar radiation data to estimate the monthly average of daily global solar irradiation on a horizontal surface based on a single parameter, sunshine hours, using the artificial neural network method. The station under the study is located in Kampala, Uganda at a latitude of 0.19°N, a longitude of 32.34°E, and an altitude of 1200 m above sea level. The five-year data was split into two parts in 2003–2006 and 2007-2008; the first part was used for training, and the latter was used for testing the neural network. Amongst the models tested, the feed-forward back-propagation network with one hidden layer (65 neurons) and with the tangent sigmoid as the transfer function emerged as the more appropriate model. Results obtained using the proposed model showed good agreement between the estimated and actual values of global solar irradiation. A correlation coefficient of 0.963 was obtained with a mean bias error of 0.055 MJ/m2 and a root mean square error of 0.521 MJ/m2. The single-parameter ANN model shows promise for estimating global solar irradiation at places where monitoring stations are not established and stations where we have one common parameter (sunshine hours).