Authors: M. Benghanem, A. Mellit
Addresses: Department of Physics, Faculty of Sciences, Taibah University, P.O. Box.344 Madinah, Saudi Arabia. ' Faculty of Sciences and Technology, LAMEL, Jijel University, Ouled-aissa, P.O. Box.98, Jijel 18000, Algeria
Abstract: In this paper, a model for predicting hourly global, diffuse and direct solar irradiance is described. A dataset of measured air temperature, relative humidity, direct, diffuse and global horizontal irradiance for Madinah site (Saudi Arabia) were used in this study. Several combinations have been proposed, and the best performance is obtained by using sunshine duration, air temperature and relative humidity as inputs of the model. A good agreement between measured and predicted data is obtained. In fact, the correlation coefficient is more than 97% and the mean bias error is less than 0.8%. A comparison between artificial neural network (ANN) and the proposed model is presented in order to demonstrate its performance.
Keywords: solar irradiance; prediction; modelling; solar radiation; sunshine duration; air temperature; relative humidity; artificial neural networks; ANNs; solar energy; solar power; renewable energy.
International Journal of Renewable Energy Technology, 2011 Vol.2 No.2, pp.193 - 220
Received: 19 Aug 2009
Accepted: 06 Jul 2010
Published online: 31 Mar 2011 *