Modeling the global solar radiation on the earth's surface using atmospheric deterministic and intelligent data-driven techniques

Abstract

Three methods for analyzing and modeling the global shortwave radiation reaching the earth's surface are presented in this study. Solar radiation is a very important input for many aspects of climatology, hydrology, atmospheric sciences, and energy applications. The estimation methods consist of an atmospheric deterministic model and two data-driven intelligent methods. The deterministic method is a broadband atmospheric model, developed for predicting the global and diffuse solar radiation incident on the earth's surface. The intelligent data-driven methods are a new neural network approach in which the hourly values of global radiation for several years are calculated and a new fuzzy logic method based on fuzzy sets theory. The two data-driven models, calculating the global solar radiation on a horizontal surface, are based on measured data of several meteorological parameters such as the air temperature, the relative humidity, and the sunshine duration. The three methods are tested and compared using various sets of solar radiation measurements. The comparison of the three methods showed that the proposed intelligent techniques can be successfully used for the estimation of global solar radiation during the warm period of the year, while during the cold period the atmospheric deterministic model gives better estimations.