Table of Contents
ISRN Renewable Energy
Volume 2012, Article ID 412471, 11 pages
http://dx.doi.org/10.5402/2012/412471
Research Article

Time-Series Regression Model for Prediction of Mean Daily Global Solar Radiation in Al-Ain, UAE

Department of Electrical Engineering, United Arab Emirates University, P.O. Box 17555, Al-Ain, UAE

Received 1 December 2011; Accepted 10 January 2012

Academic Editors: A. Hasan and S. Rehman

Copyright Β© 2012 Hassan A. N. Hejase and Ali H. Assi. 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

The availability of short-term forecast weather model for a particular country or region is essential for operation planning of energy systems. This paper presents the first step by a group of researchers at UAE University to establish a weather model for the UAE using the weather data for at least 10 years and employing various models such as classical empirical models, artificial neural network (ANN) models, and time-series regression models with autoregressive integrated moving-average (ARIMA). This work uses time-series regression with ARIMA modeling to establish a model for the mean daily and monthly global solar radiation (GSR) for the city of Al-Ain, United Arab Emirates. Time-series analysis of solar radiation has shown to yield accurate average long-term prediction performance of solar radiation in Al-Ain. The model was built using data for 10 years (1995–2004) and was validated using data of three years (2005–2007), yielding deterministic coefficients (𝑅2) of 92.6% and 99.98% for mean daily and monthly GSR data, respectively. The low corresponding values of mean bias error (MBE), mean absolute bias error (MABE), mean absolute percentage error (MAPE), and root-mean-square error (RMSE) confirm the adequacy of the obtained model for long-term prediction of GSR data in Al-Ain, UAE.