Articles | Volume 5, issue 3
Wind Energ. Sci., 5, 959–975, 2020
https://doi.org/10.5194/wes-5-959-2020

Special issue: Flow in complex terrain: the Perdigão campaigns (WES/ACP/AMT...

Wind Energ. Sci., 5, 959–975, 2020
https://doi.org/10.5194/wes-5-959-2020
Research article
26 Jul 2020
Research article | 26 Jul 2020

Decreasing wind speed extrapolation error via domain-specific feature extraction and selection

Daniel Vassallo et al.

Viewed

Total article views: 1,812 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,292 469 51 1,812 66 68
  • HTML: 1,292
  • PDF: 469
  • XML: 51
  • Total: 1,812
  • BibTeX: 66
  • EndNote: 68
Views and downloads (calculated since 25 Sep 2019)
Cumulative views and downloads (calculated since 25 Sep 2019)

Viewed (geographical distribution)

Total article views: 1,501 (including HTML, PDF, and XML) Thereof 1,446 with geography defined and 55 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 Jun 2022
Download
Short summary
Model error and uncertainty is a challenge in the wind energy industry, potentially leading to mischaracterization of millions of dollars' worth of wind resource. This paper combines meteorological knowledge with machine learning techniques, specifically artificial neural networks (ANNs), to better extrapolate wind speeds. It is found that ANNs can reduce power-law extrapolation error by up to 52 % while simultaneously reducing uncertainty. A test case is shown to help decipher the ANN results.