Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid. In this work, we develop a variety of state-of-the-art probabilistic models for forecasting solar irradiance. We investigate the use of post-hoc calibration techniques for ensuring well-calibrated probabilistic predictions. We train and evaluate the models using public data from seven stations in the SURFRAD network, and demonstrate that the best model, NGBoost, achieves higher performance at an intra-hourly resolution than the best benchmark solar irradiance forecasting model across all stations. Further, we show that NGBoost with CRUDE post-hoc calibration achieves comparable performance to a numerical weather prediction model on hourly-resolution forecasting.

翻译：推进概率太阳预报方法对于支持将太阳能纳入电网至关重要。在这项工作中,我们开发了各种最先进的预测太阳辐照性能的概率模型。我们调查了使用热后校准技术确保准确校准概率预测的使用情况。我们利用来自SARCRAD网络七个站的公共数据培训和评价模型,并证明最佳模型NGBoost在小时内分辨率比所有站点的最佳基准太阳辐照性预测模型高。此外,我们显示,与CRUDE事后校准相比,NGBoost取得了与小时分辨率预测数字天气预测模型相似的性能。