Abstract Hierarchical forecasting and reconciliation are new to the field of solar engineering. Previous papers in this series, namely, Yang et al. (2017a, 2017b), and Yagli et al. (2019b), discussed various reconciliation techniques for deterministic solar forecasts obtained across spatio-temporal hierarchies. This paper extends the discussion into probability space, and studies how reconciliation can affect the performance of probabilistic forecasting. More specifically, qualities of the parametric predictive distributions before and after reconciliation are compared. Four minimum-trace-based reconciliation techniques are used to reconcile day-ahead and hour-ahead forecasts generated using two datasets: (1) distributed solar power generation for 318 simulated PV systems in California, and (2) satellite-derived irradiance over Arizona. The empirical result shows that reconciliation not only improves the accuracy of point forecasts, but also leads to high-quality predictive distributions in terms of sharpness, calibration, and skill score. Moreover, such improvement is quite general, and does not seem to depend on data, hierarchy structure, nor the underlying forecasting model.

中文翻译：

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协调太阳预测：在地理层次上使用同方差高斯误差进行概率预测

摘要 分层预测和协调是太阳能工程领域的新事物。本系列以前的论文，即 Yang 等人。(2017a, 2017b) 和 Yagli 等人。(2019b)，讨论了用于跨时空层次结构获得的确定性太阳预测的各种协调技术。本文将讨论扩展到概率空间，并研究协调如何影响概率预测的性能。更具体地说，比较协调前后的参数预测分布的质量。四种基于最小迹线的调节技术用于调节使用两个数据集生成的日前和前小时预测：(1) 加利福尼亚州 318 个模拟光伏系统的分布式太阳能发电，以及 (2) 亚利桑那州卫星衍生的辐照度. 实证结果表明，reconciliation 不仅提高了点预测的准确性，而且在锐度、校准和技能分数方面产生了高质量的预测分布。而且，这种改进是相当普遍的，似乎不依赖于数据、层次结构，也不依赖于底层的预测模型。