Articles | Volume 23, issue 5
https://doi.org/10.5194/acp-23-3181-2023
https://doi.org/10.5194/acp-23-3181-2023
Research article
 | 
10 Mar 2023
Research article |  | 10 Mar 2023

Estimating hub-height wind speed based on a machine learning algorithm: implications for wind energy assessment

Boming Liu, Xin Ma, Jianping Guo, Hui Li, Shikuan Jin, Yingying Ma, and Wei Gong

Viewed

Total article views: 4,701 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,500 1,139 62 4,701 92 47 58
  • HTML: 3,500
  • PDF: 1,139
  • XML: 62
  • Total: 4,701
  • Supplement: 92
  • BibTeX: 47
  • EndNote: 58
Views and downloads (calculated since 22 Sep 2022)
Cumulative views and downloads (calculated since 22 Sep 2022)

Viewed (geographical distribution)

Total article views: 4,701 (including HTML, PDF, and XML) Thereof 4,701 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 28 Mar 2025
Download
Short summary
Wind energy is one of the most essential clean and renewable forms of energy in today’s world. However, the traditional power law method generally estimates the hub-height wind speed by assuming a constant exponent between surface and hub-height wind speeds. This inevitably leads to significant uncertainties in estimating the wind speed profile. To minimize the uncertainties, we here use a machine learning algorithm known as random forest to estimate the wind speed at hub height.
Share
Altmetrics
Final-revised paper
Preprint