Prediction of the solar radiation evolution using computational intelligence techniques and cloudiness indices

In this paper, Artificial Neural Networks are applied for multi-step long term solar radiation prediction. The input-output structure of the neural network models is selected using evolutionary computation methods. The networks are trained as one-step-ahead predictors and iterated over time to obtain multi-step longer term predictions. Auto-regressive and auto-regressive with exogenous inputs models are compared, considering cloudiness indices as inputs in the latter case. These indices are obtained through pixel classification of ground-to-sky images, captured by a CCD camera.