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Comparative Analysis of Offshore Wind Resources and Optimal Wind Speed Distribution Models in China and Europe

Author

Listed:
  • Yanan Chen

    (Department of Mechanics, School of Mechanical Engineering, Tianjin University, Tianjin 300350, China)

  • Ming Zhao

    (Department of Mechanics, School of Mechanical Engineering, Tianjin University, Tianjin 300350, China
    Tianjin Key Laboratory of Modern Engineering Mechanics, Tianjin University, Tianjin 300350, China)

  • Zhengxian Liu

    (Department of Mechanics, School of Mechanical Engineering, Tianjin University, Tianjin 300350, China
    Tianjin Key Laboratory of Modern Engineering Mechanics, Tianjin University, Tianjin 300350, China)

  • Jianlong Ma

    (School of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
    Key Laboratory of Wind Energy and Solar Energy Technology, Ministry of Education, Hohhot 010051, China
    Engineering Research Center of Renewable Energy at Universities of Inner Mongolia Autonomous Region, Hohhot 010051, China)

  • Lei Yang

    (DongFang Electric Wind Power Co., Ltd., Deyang 618000, China)

Abstract

Offshore wind resources in China and Europe are systematically compared, focusing on wind speed characteristics and the selection of optimal wind speed probability distribution models. Using 20 years of data at 10 m and 100 m above sea level, seven unimodal wind speed probability distribution models were applied. The results point out that China’s offshore wind resources exhibit high spatial and temporal variability, influenced by monsoons and typhoons, while European seas are characterized by stable wind patterns. Among the models tested, the Weibull distribution is the most accurate one for wind speed fitting, while the Generalized Extreme Value and Gamma models perform better in regions with higher skewness and extreme wind events. This study highlights the importance of wind speed characteristics, such as skewness and kurtosis, in selecting the optimal model. These findings provide valuable guidance for the improvement of offshore wind energy assessments and the selection of appropriate models. Future research should explore advanced techniques, such as machine learning and hybrid models, to better capture complex wind patterns and enhance model accuracy.

Suggested Citation

  • Yanan Chen & Ming Zhao & Zhengxian Liu & Jianlong Ma & Lei Yang, 2025. "Comparative Analysis of Offshore Wind Resources and Optimal Wind Speed Distribution Models in China and Europe," Energies, MDPI, vol. 18(5), pages 1-51, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1108-:d:1598759
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    References listed on IDEAS

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    1. Liu, Yichao & Chen, Daoyi & Li, Sunwei & Chan, P.W., 2018. "Discerning the spatial variations in offshore wind resources along the coast of China via dynamic downscaling," Energy, Elsevier, vol. 160(C), pages 582-596.
    2. Han, Qinkai & Ma, Sai & Wang, Tianyang & Chu, Fulei, 2019. "Kernel density estimation model for wind speed probability distribution with applicability to wind energy assessment in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    3. Hayes, Liam & Stocks, Matthew & Blakers, Andrew, 2021. "Accurate long-term power generation model for offshore wind farms in Europe using ERA5 reanalysis," Energy, Elsevier, vol. 229(C).
    4. Wais, Piotr, 2017. "Two and three-parameter Weibull distribution in available wind power analysis," Renewable Energy, Elsevier, vol. 103(C), pages 15-29.
    5. Zhang, Hua & Yu, Yong-Jing & Liu, Zhi-Yuan, 2014. "Study on the Maximum Entropy Principle applied to the annual wind speed probability distribution: A case study for observations of intertidal zone anemometer towers of Rudong in East China Sea," Applied Energy, Elsevier, vol. 114(C), pages 931-938.
    6. Olauson, Jon, 2018. "ERA5: The new champion of wind power modelling?," Renewable Energy, Elsevier, vol. 126(C), pages 322-331.
    7. Wen, Yi & Kamranzad, Bahareh & Lin, Pengzhi, 2021. "Assessment of long-term offshore wind energy potential in the south and southeast coasts of China based on a 55-year dataset," Energy, Elsevier, vol. 224(C).
    8. Katikas, Loukas & Dimitriadis, Panayiotis & Koutsoyiannis, Demetris & Kontos, Themistoklis & Kyriakidis, Phaedon, 2021. "A stochastic simulation scheme for the long-term persistence, heavy-tailed and double periodic behavior of observational and reanalysis wind time-series," Applied Energy, Elsevier, vol. 295(C).
    9. Philipp Beiter & Trieu Mai & Matthew Mowers & John Bistline, 2023. "Expanded modelling scenarios to understand the role of offshore wind in decarbonizing the United States," Nature Energy, Nature, vol. 8(11), pages 1240-1249, November.
    10. Morales, J.M. & Mínguez, R. & Conejo, A.J., 2010. "A methodology to generate statistically dependent wind speed scenarios," Applied Energy, Elsevier, vol. 87(3), pages 843-855, March.
    11. Costoya, X. & deCastro, M. & Carvalho, D. & Feng, Z. & Gómez-Gesteira, M., 2021. "Climate change impacts on the future offshore wind energy resource in China," Renewable Energy, Elsevier, vol. 175(C), pages 731-747.
    12. Giovanni Gualtieri, 2021. "Reliability of ERA5 Reanalysis Data for Wind Resource Assessment: A Comparison against Tall Towers," Energies, MDPI, vol. 14(14), pages 1-21, July.
    13. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2014. "Offshore wind energy resource simulation forced by different reanalyses: Comparison with observed data in the Iberian Peninsula," Applied Energy, Elsevier, vol. 134(C), pages 57-64.
    14. Xinyang Guo & Xinyu Chen & Xia Chen & Peter Sherman & Jinyu Wen & Michael McElroy, 2023. "Grid integration feasibility and investment planning of offshore wind power under carbon-neutral transition in China," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    15. Carlos Tenreiro, 2011. "Fourier series-based direct plug-in bandwidth selectors for kernel density estimation," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(2), pages 533-545.
    16. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren, 2019. "Characterization of wind resource in China from a new perspective," Energy, Elsevier, vol. 167(C), pages 994-1010.
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