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.

最近的研究表明，不同预测模型的整合或混合能够改进对太阳辐射的预测。然而，大多数工作执行模型混合以改进点预测，但预测模型的集成以改进概率预测并没有受到太多关注。在这项工作中，解决了对四种全球水平辐照度 (GHI) 预测模型（智能持续性、WRF-太阳能、CIADcast 和卫星）集成的预测间隔的估计。已经分析了最多提前一小时的几个短期预测范围。在此背景下，本文的目的之一是研究与天气稳定性相关的天气天气条件的知识是否有助于减少由预测间隔表示的不确定性。为了解决这个问题，混合模型使用了预测时出现哪种天气类型的信息。已经考虑了四种天气类型。下限估计方法的多目标变体已在这项工作中用于预测区间估计，并与两种基线方法进行比较：分位数回归 (QR) 和梯度提升 (GBR)。使用在伊比利亚半岛南部塞维利亚登记的数据进行了详尽的实验验证。结果表明，根据所使用的所有性能指标，一般而言，使用天气类型信息会降低预测间隔的不确定性。更具体地说，对于高覆盖率 (0.90, 0.95) 预测区间，关于指标之一（区间覆盖率和宽度之间的比率），使用天气类型可将多目标方法的比率提高2％–3％。此外，将多目标方法与高覆盖区间的两个基线进行比较，比 QR 提高了 11%–17%，比 GBR 提高了 10%–44%。低覆盖区间 (0.85) 的改进较小。