Articles | Volume 23, issue 5
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

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Extending wind profile beyond the surface layer by combining physical and machine learning approaches
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A comprehensive reappraisal of long-term aerosol characteristics, trends, and variability in Asia
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Cited articles

Abbes, M. and Belhadj, J.: Wind resource estimation and wind park design in El-Kef region, Tunisia. Energy, 40, 348–357,, 2012. 
Akpinar, E. K. and Akpinar, S.: An assessment on seasonal analysis of wind energy characteristics and wind turbine characteristics, Energy Convers. Manage., 46, 1848–1867,, 2005. 
Ali, S., Lee, S. M., and Jang, C. M.: Statistical analysis of wind characteristics using Weibull and Rayleigh distributions in Deokjeok-do Island–Incheon, South Korea, Renew. Energ., 123, 652–663,, 2018. 
Allabakash, S., Lim, S., Yasodha, P., Kim, H., and Lee, G.: Intermittent clutter suppression method based on adaptive harmonic wavelet transform for L-band radar wind profiler, IEEE T. Geosci. Remote, 57, 8546–8556, 2019. 
Banuelos-Ruedas, F., Angeles-Camacho, C., and Rios-Marcuello, S.: Analysis and validation of the methodology used in the extrapolation of wind speed data at different heights, Renew. Sustain. Energy Rev., 14, 2383–2391,, 2010. 
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.
Final-revised paper