Accurate production forecasts are essential to continue facilitating the integration of renewable energy sources into the power grid. This paper illustrates how to obtain probabilistic day-ahead forecasts of wind power generation via gradient boosting trees using an ensemble of weather forecasts. To this end, we perform a comparative analysis across three state-of-the-art probabilistic prediction methods-conformalised quantile regression, natural gradient boosting and conditional diffusion models-all of which can be combined with tree-based machine learning. The methods are validated using four years of data for all wind farms present within the Belgian offshore zone. Additionally, the point forecasts are benchmarked against deterministic engineering methods, using either the power curve or an advanced approach incorporating a calibrated analytical wake model. The experimental results show that the machine learning methods improve the mean absolute error by up to 53% and 33% compared to the power curve and the calibrated wake model. Considering the three probabilistic prediction methods, the conditional diffusion model is found to yield the best overall probabilistic and point estimate of wind power generation. Moreover, the findings suggest that the use of an ensemble of weather forecasts can improve point forecast accuracy by up to 23%.

翻译：精确的生产预测对于持续促进可再生能源并网至关重要。本文阐述了如何利用天气预测集合，通过梯度提升树获得风电功率的概率性日前预测。为此，我们对三种先进的概率预测方法——保形化分位数回归、自然梯度提升和条件扩散模型——进行了比较分析，这些方法均可与基于树状的机器学习相结合。利用比利时近海区域所有风电场四年的数据对所提方法进行了验证。此外，将点预测结果与确定性工程方法（包括功率曲线法和结合了标定解析尾流模型的先进方法）进行了基准比较。实验结果表明，相较于功率曲线法和标定尾流模型法，机器学习方法将平均绝对误差分别降低了最高达53%和33%。在三种概率预测方法中，条件扩散模型被证明能提供最优的整体概率性及点估计风电功率预测结果。此外，研究结果表明，使用天气预测集合可将点预测准确率提升最高达23%。