Articles | Volume 5, issue 2
Wind Energ. Sci., 5, 489–501, 2020
https://doi.org/10.5194/wes-5-489-2020
Wind Energ. Sci., 5, 489–501, 2020
https://doi.org/10.5194/wes-5-489-2020

Research article 17 Apr 2020

Research article | 17 Apr 2020

The importance of round-robin validation when assessing machine-learning-based vertical extrapolation of wind speeds

Nicola Bodini and Mike Optis

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Nicola Bodini on behalf of the Authors (05 Mar 2020)  Author's response    Manuscript
ED: Publish as is (20 Mar 2020) by Christian Masson
ED: Publish as is (23 Mar 2020) by Gerard J.W. van Bussel(Chief Editor)
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Short summary
An accurate assessment of the wind resource at hub height is necessary for an efficient and bankable wind farm project. Conventional techniques for wind speed vertical extrapolation include a power law and a logarithmic law. Here, we propose a round-robin validation to assess the benefits that a machine-learning-based approach can provide in vertically extrapolating wind speed at a location different from the training site – the most practically useful application for the wind energy industry.