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

Data sets

ERA5 hourly data on single levels from 1959 to present ECMWF\#!/dataset/reanalysis-era5-single-levels?tab=overview

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