Smart baseline models for solar irradiation forecasting

Significant improvements were obtained over the basic persistence methods with both approaches. In the case of moving models, results proved that the best approach to update the calibration set was by computing the Euclidean distance in the principal components space. Results of both approaches were comparable in terms of MAE and forecast skill (s), though slightly superior predictions were obtained with the moving SVR, with a forecast skill ranging from 8% to 23% and a testing MAE ranging from 49 to 64 W/m2 for the different states of cloudiness. Anyway, both approaches are valid baselines to compare new forecasting models fed with more difficult-to-obtain features, supplementing the classic but naive persistence models.