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

Peak wind gust (inline-formulaW_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 inline-formulaW_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 inline-formulaW_p series. Through a systematic feature importance study, we also identify the most relevant meteorological variables for inline-formulaW_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., inline-formulaW_p>20 m sinline-formula⁻¹) is less satisfactory.