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

Related authors

Extending the wind profile beyond the surface layer by combining physical and machine learning approaches
Boming Liu, Xin Ma, Jianping Guo, Renqiang Wen, Hui Li, Shikuan Jin, Yingying Ma, Xiaoran Guo, and Wei Gong
Atmos. Chem. Phys., 24, 4047–4063, https://doi.org/10.5194/acp-24-4047-2024,https://doi.org/10.5194/acp-24-4047-2024, 2024
Short summary
A comprehensive reappraisal of long-term aerosol characteristics, trends, and variability in Asia
Shikuan Jin, Yingying Ma, Zhongwei Huang, Jianping Huang, Wei Gong, Boming Liu, Weiyan Wang, Ruonan Fan, and Hui Li
Atmos. Chem. Phys., 23, 8187–8210, https://doi.org/10.5194/acp-23-8187-2023,https://doi.org/10.5194/acp-23-8187-2023, 2023
Short summary
Performance evaluation for retrieving aerosol optical depth from the Directional Polarimetric Camera (DPC) based on the GRASP algorithm
Shikuan Jin, Yingying Ma, Cheng Chen, Oleg Dubovik, Jin Hong, Boming Liu, and Wei Gong
Atmos. Meas. Tech., 15, 4323–4337, https://doi.org/10.5194/amt-15-4323-2022,https://doi.org/10.5194/amt-15-4323-2022, 2022
Short summary
Carbon dioxide cover: carbon dioxide column concentration seamlessly distributed globally during 2009–2020
Haowei Zhang, Boming Liu, Xin Ma, Ge Han, Qinglin Yang, Yichi Zhang, Tianqi Shi, Jianye Yuan, Wanqi Zhong, Yanran Peng, Jingjing Xu, and Wei Gong
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-215,https://doi.org/10.5194/essd-2022-215, 2022
Preprint withdrawn
Short summary
Intercomparison of wind observations from ESA’s satellite mission Aeolus, ERA5 reanalysis and radiosonde over China
Boming Liu, Jianping Guo, Wei Gong, Yong Zhang, Lijuan Shi, Yingying Ma, Jian Li, Xiaoran Guo, Ad Stoffelen, Gerrit de Leeuw, and Xiaofeng Xu
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-26,https://doi.org/10.5194/amt-2022-26, 2022
Publication in AMT not foreseen
Short summary

Related subject area

Subject: Aerosols | Research Activity: Field Measurements | Altitude Range: Troposphere | Science Focus: Chemistry (chemical composition and reactions)
The annual cycle and sources of relevant aerosol precursor vapors in the central Arctic during the MOSAiC expedition
Matthew Boyer, Diego Aliaga, Lauriane L. J. Quéléver, Silvia Bucci, Hélène Angot, Lubna Dada, Benjamin Heutte, Lisa Beck, Marina Duetsch, Andreas Stohl, Ivo Beck, Tiia Laurila, Nina Sarnela, Roseline C. Thakur, Branka Miljevic, Markku Kulmala, Tuukka Petäjä, Mikko Sipilä, Julia Schmale, and Tuija Jokinen
Atmos. Chem. Phys., 24, 12595–12621, https://doi.org/10.5194/acp-24-12595-2024,https://doi.org/10.5194/acp-24-12595-2024, 2024
Short summary
Opinion: How will advances in aerosol science inform our understanding of the health impacts of outdoor particulate pollution?
Imad El Haddad, Danielle Vienneau, Kaspar R. Daellenbach, Robin Modini, Jay G. Slowik, Abhishek Upadhyay, Petros N. Vasilakos, David Bell, Kees de Hoogh, and Andre S. H. Prevot
Atmos. Chem. Phys., 24, 11981–12011, https://doi.org/10.5194/acp-24-11981-2024,https://doi.org/10.5194/acp-24-11981-2024, 2024
Short summary
Measurement report: Intra-annual variability of black carbon and brown carbon and their interrelation with meteorological conditions over Gangtok, Sikkim
Pramod Kumar, Khushboo Sharma, Ankita Malu, Rajeev Rajak, Aparna Gupta, Bidyutjyoti Baruah, Shailesh Yadav, Thupstan Angchuk, Jayant Sharma, Rakesh Kumar Ranjan, Anil Kumar Misra, and Nishchal Wanjari
Atmos. Chem. Phys., 24, 11585–11601, https://doi.org/10.5194/acp-24-11585-2024,https://doi.org/10.5194/acp-24-11585-2024, 2024
Short summary
Long-range transport of air pollutants increases the concentration of hazardous components of PM2.5 in northern South America
Maria P. Velásquez-García, K. Santiago Hernández, James A. Vergara-Correa, Richard J. Pope, Miriam Gómez-Marín, and Angela M. Rendón
Atmos. Chem. Phys., 24, 11497–11520, https://doi.org/10.5194/acp-24-11497-2024,https://doi.org/10.5194/acp-24-11497-2024, 2024
Short summary
Dominant influence of biomass combustion and cross-border transport on nitrogen-containing organic compound levels in the southeastern Tibetan Plateau
Meng Wang, Qiyuan Wang, Steven Sai Hang Ho, Jie Tian, Yong Zhang, Shun-cheng Lee, and Junji Cao
Atmos. Chem. Phys., 24, 11175–11189, https://doi.org/10.5194/acp-24-11175-2024,https://doi.org/10.5194/acp-24-11175-2024, 2024
Short summary

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. 
Download
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.
Altmetrics
Final-revised paper
Preprint