Wind Energy Science
Wind Energy Science
WES
Articles | Volume 6, issue 6
Articles | Volume 6, issue 6
https://doi.org/10.5194/wes-6-1363-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-6-1363-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 6, issue 6
Research article
 | 
03 Nov 2021
Research article |  | 03 Nov 2021

Assessing boundary condition and parametric uncertainty in numerical-weather-prediction-modeled, long-term offshore wind speed through machine learning and analog ensemble

Nicola Bodini, Weiming Hu, Mike Optis, Guido Cervone, and Stefano Alessandrini

Viewed

Total article views: 4,183 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,843 1,216 124 4,183 144 136 153
  • HTML: 2,843
  • PDF: 1,216
  • XML: 124
  • Total: 4,183
  • Supplement: 144
  • BibTeX: 136
  • EndNote: 153
Views and downloads (calculated since 11 May 2021)
Cumulative views and downloads (calculated since 11 May 2021)

Viewed (geographical distribution)

Total article views: 4,183 (including HTML, PDF, and XML) Thereof 4,093 with geography defined and 90 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 16 Mar 2026
Download
Short summary
We develop two machine-learning-based approaches to temporally extrapolate uncertainty in hub-height wind speed modeled by a numerical weather prediction model. We test our approaches in the California Outer Continental Shelf, where a significant offshore wind energy development is currently being planned, and we find that both provide accurate results.
Read more
Share
Altmetrics
Final-revised paper
Preprint