Table of Contents
Advances in Artificial Neural Systems
Volume 2011 (2011), Article ID 142054, 6 pages
http://dx.doi.org/10.1155/2011/142054
Research Article

Using Artificial Neural Networks to Predict Direct Solar Irradiation

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

Received 30 May 2011; Accepted 2 August 2011

Academic Editor: Matt Aitkenhead

Copyright © 2011 James Mubiru. 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

This paper explores the possibility of developing a prediction model using artificial neural networks (ANNs), which could be used to estimate monthly average daily direct solar radiation for locations in Uganda. Direct solar radiation is a component of the global solar radiation and is quite significant in the performance assessment of various solar energy applications. Results from the paper have shown good agreement between the estimated and measured values of direct solar irradiation. A correlation coefficient of 0.998 was obtained with mean bias error of 0.005 MJ/m2 and root mean square error of 0.197 MJ/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.