Wind Energy Science
Wind Energy Science
WES
Articles | Volume 9, issue 7
Articles | Volume 9, issue 7
https://doi.org/10.5194/wes-9-1507-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-9-1507-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 9, issue 7
Research article
 | 
12 Jul 2024
Research article |  | 12 Jul 2024

Hyperparameter tuning framework for calibrating analytical wake models using SCADA data of an offshore wind farm

Diederik van Binsbergen, Pieter-Jan Daems, Timothy Verstraeten, Amir R. Nejad, and Jan Helsen

Viewed

Total article views: 3,021 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,246 704 71 3,021 85 109
  • HTML: 2,246
  • PDF: 704
  • XML: 71
  • Total: 3,021
  • BibTeX: 85
  • EndNote: 109
Views and downloads (calculated since 21 Aug 2023)
Cumulative views and downloads (calculated since 21 Aug 2023)

Viewed (geographical distribution)

Total article views: 3,021 (including HTML, PDF, and XML) Thereof 2,972 with geography defined and 49 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 15 Nov 2025
Download
Short summary
Wind farm yield assessment often relies on analytical wake models. Calibrating these models can be challenging due to the stochastic nature of wind. We developed a calibration framework that performs a multi-phase optimization on the tuning parameters using time series SCADA data. This yields a parameter distribution that more accurately reflects reality than a single value. Results revealed notable variation in resultant parameter values, influenced by nearby wind farms and coastal effects.
Read more
Share
Altmetrics
Final-revised paper
Preprint