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 the 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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Preprint withdrawn
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Publication in AMT not foreseen
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Subject: Aerosols | Research Activity: Field Measurements | Altitude Range: Troposphere | Science Focus: Chemistry (chemical composition and reactions)
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Cited articles

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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.
Final-revised paper