Abstract The present paper echos a recent data article, “A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods” [J. Renewable Sustainable Energy 11, 036102 (2019)]. The carefully composed dataset by Pedro, Larson, and Coimbra (PLC) presents a rare opportunity for solar forecasters to develop transparent and reproducible algorithms that can bring incremental contributions to the field. In their original paper, data from four different sources, namely, ground-based measurements, sky-camera images, satellite-imagery features, and numerical weather prediction outputs, were arranged in a machine-learning-ready setup. Subsequently, several benchmarks for deterministic forecasting were set forth, for intra-hour, intra-day, and day-ahead scenarios. Nonetheless, a weather forecast is intrinsically five-dimensional, spanning space, time, and probability. In this regard, five reference methods for probabilistic forecasting: (1) complete-history persistence ensemble, (2) Markov-chain mixture model, (3) ordinary least squares, (4) analog ensemble, and (5) quantile regression, are applied to the PLC dataset. The R code provided in this paper follows the structure of the original Python code precisely, facilitating those solar forecasters who are not familiar with Python but have a statistics background.

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加利福尼亚州福尔瑟姆标准化数据集上的概率太阳预测基准

摘要 本文呼应了最近的一篇数据文章，“用于加速开发和确定太阳预测方法基准的综合数据集”[J. 可再生可持续能源 11, 036102 (2019)]。Pedro、Larson 和 Coimbra (PLC) 精心编写的数据集为太阳能预报员提供了一个难得的机会，可以开发透明和可重复的算法，为该领域带来增量贡献。在他们的原始论文中，来自四个不同来源的数据，即地面测量、天空相机图像、卫星图像特征和数值天气预报输出，被安排在一个机器学习就绪的设置中。随后，针对小时内、日内和日前情景提出了几个确定性预测的基准。尽管如此，天气预报本质上是五维的，跨越空间、时间和概率。在这方面，概率预测的五种参考方法：（1）完整历史持久性集成，（2）马尔可夫链混合模型，（3）普通最小二乘法，（4）模拟集成，以及（5）分位数回归应用于 PLC 数据集。本文提供的R代码准确遵循了原始Python代码的结构，方便不熟悉Python但有统计背景的太阳预报员使用。