SOLAR RADIATION FORECAST USING ARTIFICAL NEURAL NETWORKS IN SOUTH BRAZIL

Forecasts from Eta/CPTEC model, expressing the future atmospheric conditions, are used as inputs in Artificial Neural Networks (ANNs), in order to achieve more reliable short-term forecasts for the incident solar radiation. Global solar radiation measurements performed by two stations of the SONDA project located in south Brazil (Florianopolis and Sao Martinho da Serra) are used as the targets during ANNs training and for forecasts evaluation. Solar radiation forecasts from ANNs present higher correlation coefficients and lower errors than the Eta model output for shortwave radiation on ground. The well-know bias observed in solar radiation forecasts by the Eta model was removed by the use of ANNs. The improvement in RMSE obtained with ANNs over the Eta model was higher than 30%, estimated with a skillscore. This improvement is a response to a constant demand from the energy sector for more accurate ways of forecasting the solar energy power, so as to support the management of the national generation and distribution systems of electricity.