Solar radiation estimation methods using ANN and empirical models

Abstract

Many empirical equations and methods have been used and proposed in order to estimate the solar radiation (Rs). In this work, empirical equations, such as Hargreaves method, Artificial Neural Networks (ANN) technology and multi-linear regression methods (MLR) were used to estimate solar radiation. The daily meteorological measurements of air temperature, radiation, humidity and wind velocity from the stations of Aristotle University Farm and Amintaio in Northern Greece were used to derive the solar radiation models. The measurements of Rs were used to derive new and to evaluate existing models. Different combinations of input variables were examined in the ANN models, and in the MLR models different variables were used. The results of RMSE criteria of the examined models showed that they are in the same range with many other models describing Rs as summarized in many review articles. The use of extraterrestrial radiation and the square root of daily difference in temperature in the ANN and MLR models improve the accuracy of the results. The results of ANN models in comparison to MLR models using the same input variables are consistent between them.