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

Serkan Kartal*, Sukanta Basu, Simon J. Watson

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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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.
Original languageEnglish
Article number8
Pages (from-to)1533–1551
Number of pages19
JournalWind Energy Science
Volume8
Issue number10
DOIs
Publication statusPublished - 2023

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