Evolutionary-based prediction interval estimation by blending solar radiation forecasting models using meteorological weather types

Recent research has shown that the integration or blending of different forecasting models is able to improve the predictions of solar radiation. However, most works perform model blending to improve point forecasts, but the integration of forecasting models to improve probabilistic forecasting has not received much attention. In this work the estimation of prediction intervals for the integration of four Global Horizontal Irradiance (GHI) forecasting models (Smart Persistence, WRF-solar, CIADcast, and Satellite) is addressed. Several short-term forecasting horizons, up to one hour ahead, have been analyzed. Within this context, one of the aims of the article is to study whether knowledge about the synoptic weather conditions, which are related to the stability of weather, might help to reduce the uncertainty represented by prediction intervals. In order to deal with this issue, information about which weather type is present at the time of prediction, has been used by the blending model. Four weather types have been considered. A multi-objective variant of the Lower Upper Bound Estimation approach has been used in this work for prediction interval estimation and compared with two baseline methods: Quantile Regression (QR) and Gradient Boosting (GBR). An exhaustive experimental validation has been carried out, using data registered at Seville in the Southern Iberian Peninsula. Results show that, in general, using weather type information reduces uncertainty of prediction intervals, according to all performance metrics used. More specifically, and with respect to one of the metrics (the ratio between interval coverage and width), for high-coverage (0.90, 0.95) prediction intervals, using weather type enhances the ratio of the multi-objective approach by 2%-3%. Also, comparing the multi-objective approach versus the two baselines for high-coverage intervals, the improvement is 11%-17% over QR and 10%-44% over GBR. Improvements for low-coverage intervals (0.85) are smaller. (C) 2021 Elsevier B.V. All rights reserved.