Preprints
https://doi.org/10.5194/wes-2023-30
https://doi.org/10.5194/wes-2023-30
17 Apr 2023
 | 17 Apr 2023
Status: this preprint is currently under review for the journal WES.

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

Serkan Kartal, Sukanta Basu, and Simon J. Watson

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) that utilizes numerous inputs from a public-domain reanalysis dataset, and in turn, generates long-term, site-specific (Wp) series. Through a systematic feature importance study, we also identify the most relevant meteorological variables for (Wp) estimation. Even though the proposed INTRIGUE approach performs very well for nominal conditions compared to specific baselines, its performance for extreme conditions is less than satisfactory.

Serkan Kartal et al.

Status: open (until 04 Jun 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2023-30', Anonymous Referee #1, 10 May 2023 reply
  • RC2: 'Comment on wes-2023-30', Anonymous Referee #2, 21 May 2023 reply

Serkan Kartal et al.

Serkan Kartal et al.

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Short summary
The peak wind gust is an essential meteorological variable for wind farm planning and operations. Unfortunately, many wind farms do not have on-site measurements of it. In this paper, we propose a machine-learning approach (called INTRIGUE) that utilizes numerous inputs from a public-domain reanalysis dataset, generating long-term, site-specific peak wind gust series.