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

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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, https://doi.org/10.1016/j.energy.2012.01.061, 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, https://doi.org/10.1016/j.enconman.2004.08.012, 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, https://doi.org/10.1016/j.renene.2018.02.087, 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, https://doi.org/10.1016/j.rser.2010.05.001, 2010. 
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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.
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