Abstract With increased penetration of solar energy sources, solar power forecasting has become more crucial and challenging. This paper proposes a copula-based Bayesian approach to improve probabilistic solar power forecasting by capturing the joint distribution between solar power and ambient temperature. A prior forecast distribution is first obtained using different underlying point forecasting models. Parametric and empirical copulas of solar power and temperature are then developed to update the prior distribution to the posterior forecast distribution. A public solar power database is used to demonstrate effectiveness of the proposed method. Numerical results show that the copula-based Bayesian method outperforms the forecasting method that directly uses temperature as a feature. The Bayesian method is also compared with persistent models and show improved performance. This article includes supplementary material (data and code) for reproducibility.

中文翻译：

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一种基于 copula 的贝叶斯概率太阳能预测方法

摘要 随着太阳能资源的日益普及，太阳能功率预测变得越来越重要和具有挑战性。本文提出了一种基于 copula 的贝叶斯方法，通过捕获太阳能和环境温度之间的联合分布来改进概率太阳能预测。首先使用不同的基础点预测模型获得先验预测分布。然后开发太阳能和温度的参数和经验联结以将先验分布更新为后验预测分布。公共太阳能数据库用于证明所提出方法的有效性。数值结果表明，基于copula的贝叶斯方法优于直接以温度为特征的预测方法。贝叶斯方法还与持久模型进行了比较并显示了改进的性能。本文包括可重复性的补充材料（数据和代码）。