Short-term probabilistic solar forecasts are an important tool in decision-making processes in which uncertainty plays a non-negligible role. Purely statistical models that produce temporal or spatiotemporal probabilistic solar forecasts are generally trained individually, and the combined forecasts therefore lack the temporal or spatiotemporal correlation present in the data. To recover the spatiotemporal dependence structure, a copula can be employed, which constructs a multivariate distribution from which spatially and temporally correlated uniform random numbers can be sampled, which in turn can be used to generate the so-called space-time trajectories via the inverse probability integral transform. In this study, we employ the recently introduced ultra-fast preselection algorithm to leverage the spatiotemporal information present in a pyranometer network and compare its accuracy to that of quantile regression forecasts that only consider temporal information. We show that the preselection algorithm i...

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来自概率太阳预测的晴空指数时空轨迹：比较有希望的联结

短期概率太阳预报是决策过程中的重要工具，其中不确定性起着不可忽视的作用。产生时间或时空概率太阳预测的纯统计模型通常单独训练，因此组合预测缺乏数据中存在的时间或时空相关性。为了恢复时空依赖结构，可以使用 copula，它构建了一个多元分布，从中可以采样空间和时间相关的均匀随机数，反过来可以通过逆生成所谓的时空轨迹概率积分变换。在这项研究中，我们采用最近推出的超快速预选算法来利用总辐射表网络中存在的时空信息，并将其准确性与仅考虑时间信息的分位数回归预测的准确性进行比较。我们证明了预选算法...