Wind energy resource assessment typically requires numerical models, but such models are too computationally intensive to consider multi-year timescales. Increasingly, unsupervised machine learning techniques are used to identify a small number of representative weather patterns to simulate long-term behaviour. Here we develop a novel wind energy workflow that for the first time combines weather patterns derived from unsupervised clustering techniques with numerical weather prediction models (here WRF) to obtain efficient and accurate long-term predictions of power and downstream wakes from an entire wind farm. We use ERA5 reanalysis data clustering not only on low altitude pressure but also, for the first time, on the more relevant variable of wind velocity. We also compare the use of large-scale and local-scale domains for clustering. A WRF simulation is run at each of the cluster centres and the results are aggregated using a novel post-processing technique. By applying our workflow to two different regions, we show that our long-term predictions agree with those from a year of WRF simulations but require less than 2% of the computational time. The most accurate results are obtained when clustering on wind velocity. Moreover, clustering over the Europe-wide domain is sufficient for predicting wind farm power output, but downstream wake predictions benefit from the use of smaller domains. Finally, we show that these downstream wakes can affect the local weather patterns. Our approach facilitates multi-year predictions of power output and downstream farm wakes, by providing a fast, accurate and flexible methodology that is applicable to any global region. Moreover, these accurate long-term predictions of downstream wakes provide the first tool to help mitigate the effects of wind energy loss downstream of wind farms, since they can be used to determine optimum wind farm locations.

翻译：风能资源评估通常需要数值模型，但此类模型因计算量过大而难以实现多年时间尺度的模拟。近年来，无监督机器学习技术越来越多地被用于识别少量代表性气象模式以模拟长期行为。本文首次开发了一种新型风能工作流程，该流程将基于无监督聚类技术的气象模式与数值天气预报模型（本文采用WRF模型）相结合，实现对完整风电场功率及下游尾流的长期高效精准预测。我们首次不仅基于低海拔气压，更基于风速这一更具相关性的变量对ERA5再分析数据进行聚类分析，同时比较了采用大尺度与小尺度区域进行聚类的效果。在各类簇中心分别运行WRF模拟，并通过新型后处理技术聚合结果。通过将本工作流程应用于两个不同区域，我们发现长期预测结果与全年WRF模拟结果高度吻合，但计算耗时不足其2%。基于风速的聚类可获得最优精度。此外，覆盖欧洲全境的聚类足以预测风电场功率输出，而下游尾流预测则需采用更小区域聚类。最后证实，下游尾流会反作用于局部气象模式。本方法通过快速、精确且灵活的技术框架，适用于全球任何区域的功率输出与下游尾流多年预测。更重要的是，精准的下游尾流长期预测为减缓风电场下游风能损失效应提供了首个实用工具，可据此优化风电场选址。