Using measured daily meteorological parameters to predict daily solar radiation

A major factor for an efficient design of solar energy systems is to provide accurate estimations of the solar radiation. Many of the existing studies are focused on the analysis of monthly or annual solar radiation. This is while less attention has been paid to the determination of daily solar radiation. Accordingly, the main goal of this paper is to develop a robust machine learning approach, based on genetic programming (GP), for the estimation of the daily solar radiation. The solar radiation is formulated in terms of daily air temperature, relative humidity, atmospheric pressure, wind speed, and earth temperature. A comprehensive database containing about 7000 records collected for about 20 years (1995–2014) in a nominal city in Iran is used to develop the GP model. The performance of the derived model is verified using different criteria. A multiple linear regression analysis is performed to benchmark the GP model with a classical technique. The influences of the input variables on the solar energy are evaluated through a sensitivity analysis. The proposed model has a very good prediction performance and significantly outperforms the traditional regression model.