Articles | Volume 8, issue 5
https://doi.org/10.5194/wes-8-771-2023
https://doi.org/10.5194/wes-8-771-2023
Research article
 | 
17 May 2023
Research article |  | 17 May 2023

Gaussian mixture models for the optimal sparse sampling of offshore wind resource

Robin Marcille, Maxime Thiébaut, Pierre Tandeo, and Jean-François Filipot

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2022-39', Sarah Barber, 20 Jul 2022
    • AC1: 'Reply on RC1', Robin Marcille, 25 Oct 2022
  • RC2: 'Comment on wes-2022-39', Anonymous Referee #2, 29 Aug 2022
    • AC2: 'Reply on RC2', Robin Marcille, 25 Oct 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Robin Marcille on behalf of the Authors (25 Oct 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (04 Nov 2022) by Sara C. Pryor
RR by Anonymous Referee #3 (09 Feb 2023)
RR by Anonymous Referee #4 (20 Feb 2023)
ED: Reconsider after major revisions (20 Feb 2023) by Sara C. Pryor
AR by Robin Marcille on behalf of the Authors (15 Mar 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (09 Apr 2023) by Sara C. Pryor
ED: Publish as is (15 Apr 2023) by Joachim Peinke (Chief editor)
AR by Robin Marcille on behalf of the Authors (17 Apr 2023)  Manuscript 
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Short summary
A novel data-driven method is proposed to design an optimal sensor network for the reconstruction of offshore wind resources. Based on unsupervised learning of numerical weather prediction wind data, it provides a simple yet efficient method for the siting of sensors, outperforming state-of-the-art methods for this application. It is applied in the main French offshore wind energy development areas to provide guidelines for the deployment of floating lidars for wind resource assessment.
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