Splitting Global Solar Radiation into Diffuse and Direct Normal Fractions Using Artificial Neural Networks

Abstract The present study utilizes the measured global solar radiation, ambient temperature, and relative humidity data from Al-Hasa, Al-Jouf, and Sharurah radiation data collection stations between 1998 and 2002 to estimate the fractions of diffuse solar radiation and direct normal radiation. For estimation purposes, a radial basis function neural network has been utilized. Specifically, the reported work developed a model with a four input parameter model, i.e., day of the year, global solar radiation, ambient temperature, and relative humidity for the estimation of diffuse solar radiation and direct normal radiation fractions of solar radiation from global solar radiation. The models so developed were trained with the measured data during the period 1998 to 2001, while the data for the year 2002 was used for testing the model results.