Wind Energy Science
Wind Energy Science
WES
Articles | Volume 9, issue 4
Articles | Volume 9, issue 4
https://doi.org/10.5194/wes-9-821-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-9-821-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 9, issue 4
Research article
 | 
08 Apr 2024
Research article |  | 08 Apr 2024

Machine learning methods to improve spatial predictions of coastal wind speed profiles and low-level jets using single-level ERA5 data

Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Ville Vakkari, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée

Viewed

Total article views: 863 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
649 191 23 863 87 14 12
  • HTML: 649
  • PDF: 191
  • XML: 23
  • Total: 863
  • Supplement: 87
  • BibTeX: 14
  • EndNote: 12
Views and downloads (calculated since 10 Oct 2023)
Cumulative views and downloads (calculated since 10 Oct 2023)

Viewed (geographical distribution)

Total article views: 863 (including HTML, PDF, and XML) Thereof 839 with geography defined and 24 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 16 May 2024
Download
Short summary
Knowing the wind speed across the rotor of a wind turbine is key in making good predictions of the power production. However, models struggle to capture both the speed and the shape of the wind profile. Using machine learning methods based on the model data, we show that the predictions can be improved drastically. The work focuses on three coastal sites, spread over the Northern Hemisphere (the Baltic Sea, the North Sea, and the US Atlantic coast) with similar results for all sites.
Read more
Altmetrics
Final-revised paper
Preprint