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Probabilistic solar power forecasting based on weather scenario generation

机译：基于天气场景的概率太阳能预测

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摘要

Probabilistic solar power forecasting plays an important role in solar power grid integration and power system operations. One of the most popular probabilistic solar forecasting methods is to feed simulated explanatory weather scenarios into a deterministic forecasting model. However, the correlation among different explanatory weather variables are seldom considered during the scenario generation process. This paper presents an improved probabilistic solar power forecasting framework based on correlated weather scenario generation. Copula is used to model a multivariate joint distribution between predicted weather variables and observed weather variables. Massive weather scenarios are obtained by deriving a conditional probability density function given a current weather prediction by using the Bayesian theory. The generated weather scenarios are used as input variables to a machine learning-based multi-model solar power forecasting model, where probabilistic solar power forecasts are obtained. The effectiveness of the proposed probabilistic solar power forecasting framework is validated by using seven solar farms from the 2000-bus synthetic grid system in Texas. Numerical results of case studies at the seven sites show that the developed probabilistic solar power forecasting methodology has improved the pinball loss metric score by up to 140% compared to benchmark models.
机译：概率太阳能预测在太阳能电网集成和电力系统操作中起着重要作用。最受欢迎的概率太阳能预测方法之一是将模拟的解释性天气场景馈送到确定性预测模型中。然而，在场景生成过程中，不同的解释性天气变量之间的相关性很少考虑。本文提出了一种基于相关天气场景生成的概率太阳能预测框架。 Copula用于在预测的天气变量和观察到的天气变量之间模拟多变量的关节分布。通过使用贝叶斯理论提供当前天气预报的条件概率密度函数来获得大规模的天气场景。生成的天气场景用作输入变量到基于机器学习的多模型太阳能预测模型，其中获得了概率的太阳能预测。所提出的概率太阳能预测框架的有效性是通过在德克萨斯州2000公交车合成网格系统中使用七个太阳能电池来验证。七个地点案例研究的数值结果表明，与基准模型相比，发达的概率概率太阳能预测方法改善了最高140％的波球损失度量得分。

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