A decision-tree-based measure-correlate-predict approach for peak wind gust estimation from a global reanalysis dataset

Peak wind gust (W-p) is a crucial meteorological variable for wind farm planning and operations. However, for many wind farm sites, there is a dearth of on-site measurements of W-p. In this paper, we propose a machine-learning approach (called INTRIGUE, decIsioN-TRee-based wInd GUst Estimation) that utilizes numerous inputs from a public-domain reanalysis dataset and, in turn, generates multi-year, site-specific W-p series. Through a systematic feature importance study, we also identify the most relevant meteorological variables for W-p estimation. The INTRIGUE approach outperforms the baseline predictions for all wind gust conditions. However, the performance of this proposed approach and the baselines for extreme conditions (i.e., W-p>20 m s(-1)) is less satisfactory.