Solar radiation: Cloudiness forecasting using a soft computing approach

Solar energy is one of the most important energy sources with increasing penetration into the power supply systems of many countries, due to the reduced environmental impact of its operation. One of the important factors of the efficiency of photovoltaic systems is the predictability of solar radiation, which depends on the clouds and the meteorological conditions, the occurrence of which is a non-linear process. Prediction of clouds’ amount that affects solar radiation using Artificial Neural Networks (ANNs) is presented in this paper. To our knowledge, this approach is the first computational intelligence method that refers to cloudiness forecasting. Input parameter selection is very critical in ANN design. In this study, ten meteorological and temporal variables were selected in the implementation of the ANN structure. The appropriate ANN architecture consists of a hidden layer with hyperbolic tangent sigmoid transfer function and an output layer with a saturating linear transfer function. The most important benefits of the proposed method are the cloudiness forecasting in the next half hour, the embedment of temporal parameters for cloudiness prediction and the highest quantity of data comparatively with other similar studies.