Articles | Volume 5, issue 2
Wind Energ. Sci., 5, 489–501, 2020
Wind Energ. Sci., 5, 489–501, 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


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

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)
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.