Smart model for accurate estimation of solar radiation

Abstract

Prediction of solar radiation has drawn increasing attention in the recent years. This is because of the lack of solar radiation measurement stations. In the present work, 14 solar radiation models have been used to assess monthly global solar radiation on a horizontal surface as function of three parameters: extraterrestrial solar irradiance (G0), duration sunshine (S) and daylight hours (S0). Since it has been observed that each model is adequate for some months of the year, one model cannot be used for the prediction of the whole year. Therefore, a smart hybrid system is proposed which selects, based on the intelligent rules, the most suitable prediction model of the 14 models listed in this study. For the test and evaluation of the proposed models, Tamanrasset city, which is located in the south of Algeria, is selected for this study. The meteorological data sets of five years (2000–2004) have been collected from the Algerian National Office of Meteorology (NOM), and two spatial databases. The results indicate that the new hybrid model is capable of predicting the monthly global solar radiation, which offers an excellent measuring accuracy of R2 values ranging from 93% to 97% in this location.