There is rising interest in probabilistic forecasting to mitigate risks from solar power uncertainty, but the numerical weather prediction (NWP) ensembles readily available to system operators are often biased and underdispersed. We propose a Bayesian model averaging (BMA) post-processing method suitable for forecasting power from utility-scale photovoltaic (PV) plants at multiple time horizons up to at least the day-ahead timescale. BMA is a kernel dressing technique for NWP ensembles in which the forecast is a weighted sum of member-specific probability density functions. We tailor BMA for utility-scale PV forecasting by modeling power clipping at the AC inverter rating and advance the theory of BMA with a new beta kernel parameterization that accommodates theoretical constraints not previously addressed. BMA is demonstrated for a case study of 11 utility-scale PV plants in Texas, forecasting at hourly resolution for the complete year 2018. BMA's mixture-model approach mitigates underdispersion of the raw ensemble to significantly improve forecast calibration, while consistently outperforming an ensemble model output statistics (EMOS) parametric approach from the literature. At 4-hour lead time, the BMA post-processing achieves continuous ranked probability skill scores of 2–36% over the raw ensemble, with consistent performance at multiple lead times suitable for power system operations.

中文翻译：

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利用贝叶斯模型平均的概率太阳能预测

为了降低太阳能不确定性带来的风险，人们对概率预测的兴趣日益浓厚，但是系统运营商容易获得的数值天气预报（NWP）集合常常带有偏见且分布分散。我们提出了一种贝叶斯模型平均（BMA）后处理方法，适用于在多个时间范围内（至少提前一天）预测公用事业规模光伏（PV）工厂的电力。BMA是一种用于NWP集成的内核修整技术，其中的预测是特定于成员的概率密度函数的加权和。我们通过对交流逆变器额定功率削波建模来定制BMA以进行公用事业规模的PV预测，并通过新的Beta内核参数化来适应BMA的理论，该参数可适应以前未解决的理论约束。BMA在德克萨斯州的11个公用事业级光伏电站的案例研究中得到了演示，并预测了2018年全年的小时分辨率。BMA的混合模型方法减轻了原始集合的分散性，从而显着改善了预测校准，同时始终优于集合模型文献中的输出统计（EMOS）参数化方法。在4个小时的交付周期中，BMA后处理在原始集合中的连续排名概率技能得分达到2–36％，并且在多个交付周期中具有一致的性能，适用于电力系统运行。同时始终优于文献中的集成模型输出统计（EMOS）参数方法。在4个小时的交付周期中，BMA后处理在原始集合中的连续排名概率技能得分达到2–36％，并且在多个交付周期中具有一致的性能，适用于电力系统运行。同时始终优于文献中的集成模型输出统计（EMOS）参数方法。在4个小时的交付周期中，BMA后处理在原始集合中的连续排名概率技能得分达到2–36％，并且在多个交付周期中具有一致的性能，适用于电力系统运行。