Prediction of daily and mean monthly global solar radiation using support vector machine in an arid climate

Prior knowledge of solar radiation in situ is very important, for better management, sizing and control of solar energy installations. In this paper, an application of a support vector machine (SVM) for the prediction of daily and monthly global solar radiation on horizontal surface in Ghardaïa (Algeria) is presented. Different combinations of measured ambient temperatures, calculated maximum sunshine duration and calculated extraterrestrial solar radiation have been considered for one-step ahead prediction (one day or one month). The obtained results showed a good agreement between measured and predicted global solar radiation data. A comparative study is conducted with the developed neural networks based model and some models published in the literature. The main advantage is that the proposed SVM based models require few simple parameters to get good accuracy.