Support vector forecasting of solar radiation values

Abstract

The increasing importance of solar energy has made the accurate forecasting of radiation an important issue. In this work we apply Support Vector Regression to downscale and improve 3-hour accumulated radiation forecasts for two locations in Spain. We use either direct 3-hour SVR-refined forecasts or we build first global accumulated daily predictions and disaggregate them into 3-hour values, with both approaches outperforming the base forecasts. We also interpolate the 3-hour forecasts into hourly values using a clear sky radiation model. Here again the disaggregated SVR forecast perform better than the base ones, but the SVR advantage is now less marked. This may be because of the clear sky assumption made for interpolation not being adequate for cloudy days or because of the underlying clear sky model not being adequate enough. In any case, our study shows that machine learning methods or, more generally, hybrid artificial intelligence systems are quite relevant for solar energy prediction. © 2013 Springer-Verlag.