Wind Energy Science
Wind Energy Science
WES
Articles | Volume 5, issue 2
Articles | Volume 5, issue 2
https://doi.org/10.5194/wes-5-489-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-5-489-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 5, issue 2
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

Viewed

Total article views: 4,203 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,904 1,212 87 4,203 214 106 138
  • HTML: 2,904
  • PDF: 1,212
  • XML: 87
  • Total: 4,203
  • Supplement: 214
  • BibTeX: 106
  • EndNote: 138
Views and downloads (calculated since 14 Jan 2020)
Cumulative views and downloads (calculated since 14 Jan 2020)

Viewed (geographical distribution)

Total article views: 4,203 (including HTML, PDF, and XML) Thereof 3,688 with geography defined and 515 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 16 Nov 2025
Download
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.
Read more
Share
Altmetrics
Final-revised paper
Preprint