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

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

doi:https://doi.org/10.5194/acp-23-3181-2023

Articles | Volume 23, issue 5

https://doi.org/10.5194/acp-23-3181-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-23-3181-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Supplement

https://doi.org/10.5194/acp-23-3181-2023-supplement

Data sets

ERA5 hourly data on single levels from 1959 to present ECMWF https://cds.climate.copernicus.eu/cdsapp\#!/dataset/reanalysis-era5-single-levels?tab=overview

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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.