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- W4387687299 abstract "Abstract. Peak wind gust (Wp) 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 Wp. 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 Wp series. Through a systematic feature importance study, we also identify the most relevant meteorological variables for Wp 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., Wp>20 m s−1) is less satisfactory." @default.
- W4387687299 created "2023-10-17" @default.
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- W4387687299 date "2023-10-16" @default.
- W4387687299 modified "2023-10-18" @default.
- W4387687299 title "A decision-tree-based measure–correlate–predict approach for peak wind gust estimation from a global reanalysis dataset" @default.
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- W4387687299 doi "https://doi.org/10.5194/wes-8-1533-2023" @default.
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