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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Mar 2023
Research article |
| 10 Mar 2023
| 10 Mar 2023
Estimating hub-height wind speed based on a machine learning algorithm: implications for wind energy assessment
Boming Liu
State Key Laboratory of Information Engineering in Surveying, Mapping
and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China
Xin Ma
State Key Laboratory of Information Engineering in Surveying, Mapping
and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China
State Key Laboratory of Severe Weather, Chinese Academy of
Meteorological Sciences, Beijing 100081, China
Hui Li
State Key Laboratory of Information Engineering in Surveying, Mapping
and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China
Shikuan Jin
State Key Laboratory of Information Engineering in Surveying, Mapping
and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China
Yingying Ma
State Key Laboratory of Information Engineering in Surveying, Mapping
and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China
Wei Gong
State Key Laboratory of Information Engineering in Surveying, Mapping
and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430072, China
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- The Vertical Distributions of Aerosol Optical Characteristics Based on Lidar in Nanyang City from 2021 to 2022 M. Zhang et al. 10.3390/atmos14050894
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27 citations as recorded by crossref.
- LiDAR technology and experimental research for comprehensive measurement of atmospheric transmittance, turbulence, and wind H. Yang et al. 10.1117/1.JRS.18.012002
- Spatiotemporal variation of LAI in different vegetation types and its response to climate change in China from 2001 to 2020 Y. Ma et al. 10.1016/j.ecolind.2023.111101
- Improved Gaussian regression model for retrieving ground methane levels by considering vertical profile features H. He et al. 10.3389/feart.2024.1352498
- Quality Assessment of ERA5 Wind Speed and Its Impact on Atmosphere Environment Using Radar Profiles along the Bohai Bay Coastline C. Suo et al. 10.3390/atmos15101153
- The Vertical Distributions of Aerosol Optical Characteristics Based on Lidar in Nanyang City from 2021 to 2022 M. Zhang et al. 10.3390/atmos14050894
- Assessment of Numerical Forecasts for Hub-Height Wind Resource Parameters during an Episode of Significant Wind Speed Fluctuations J. Mo et al. 10.3390/atmos15091112
- Estimation of Planetary Boundary Layer Height From Lidar by Combining Gradient Method and Machine Learning Algorithms H. Li et al. 10.1109/TGRS.2023.3329122
- A comprehensive review on the development of data-driven methods for wind power prediction and AGC performance evaluation in wind–thermal bundled power systems S. Wang et al. 10.1016/j.egyai.2024.100336
- Machine-learning-based estimate of the wind speed over complex terrain using the long short-term memory (LSTM) recurrent neural network C. Leme Beu & E. Landulfo 10.5194/wes-9-1431-2024
- Green hydrogen potential assessment in Ghana: application of PEM electrolysis process and geospatial-multi-criteria approach M. Asare-Addo 10.1080/14786451.2023.2256892
- Extending the wind profile beyond the surface layer by combining physical and machine learning approaches B. Liu et al. 10.5194/acp-24-4047-2024
- Spatio-temporal analysis of LAI using multisource remote sensing data for source region of Yellow River Basin Y. Zhang et al. 10.3389/fenvs.2024.1320881
- The Time-Series Production Simulation in Cost Management of New Energy Grid Connection Under the Internet of Things S. Wang 10.1109/ACCESS.2024.3370162
- A novel framework for wind energy assessment at multi-time scale based on non-stationary wind speed models: A case study in China Z. Yang & S. Dong 10.1016/j.renene.2024.120406
- Quantifying strong point sources emissions of CO2 using spaceborne LiDAR: Method development and potential analysis T. Shi et al. 10.1016/j.enconman.2023.117346
- Improved Mapping of Regional Forest Heights by Combining Denoise and LightGBM Method M. Sang et al. 10.3390/rs15235436
- An Improved Method for Individual Tree Segmentation in Complex Urban Scenes Based on Using Multispectral LiDAR by Deep Learning J. Yang et al. 10.1109/JSTARS.2024.3373395
- A comprehensive reappraisal of long-term aerosol characteristics, trends, and variability in Asia S. Jin et al. 10.5194/acp-23-8187-2023
- Improving quantification of methane point source emissions from imaging spectroscopy Z. Pei et al. 10.1016/j.rse.2023.113652
- A XCO Retrieval Algorithm Coupled Spatial Correlation for the Aerosol and Carbon Detection Lidar Z. Pei et al. 10.1016/j.atmosenv.2023.119933
- Potential for wind farming in West Africa from an analysis of daily peak wind speeds and a review of low-level jet dynamics Y. Gunnell et al. 10.1016/j.rser.2023.113836
- Exploring the Conversion Model from Aerosol Extinction Coefficient to PM1, PM2.5 and PM10 Concentrations H. Shao et al. 10.3390/rs15112742
- A comprehensive assessment of wind energy potential and wind farm design in a coastal industrial city A. AlQahtani 10.1108/WJE-11-2023-0468
- Assessing the responses of different vegetation types to drought with satellite solar-induced chlorophyll fluorescence over the Yunnan-Guizhou Plateau Y. Luo et al. 10.1364/OE.501964
- Unlocking the potential: A review of artificial intelligence applications in wind energy S. Dörterler et al. 10.1111/exsy.13716
- A Cluster Analysis Approach for Nocturnal Atmospheric Boundary Layer Height Estimation from Multi-Wavelength Lidar Z. Zhu et al. 10.3390/atmos14050847
- The Potential of Lakes for Extracting Renewable Energy—A Case Study of Brates Lake in the South-East of Europe E. Rusu et al. 10.3390/inventions8060143
Latest update: 20 Nov 2024
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.
Wind energy is one of the most essential clean and renewable forms of energy in today’s world....
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