IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i5p1108-d1598759.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/5/1108/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/5/1108/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Wais, Piotr, 2017. "Two and three-parameter Weibull distribution in available wind power analysis," Renewable Energy, Elsevier, vol. 103(C), pages 15-29.
    3. 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.
    4. Olauson, Jon, 2018. "ERA5: The new champion of wind power modelling?," Renewable Energy, Elsevier, vol. 126(C), pages 322-331.
    5. 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).
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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).
    12. 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).
    13. 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).
    14. 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.
    15. 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.
    16. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gualtieri, G., 2022. "Analysing the uncertainties of reanalysis data used for wind resource assessment: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    2. de Aquino Ferreira, Saulo Custodio & Cyrino Oliveira, Fernando Luiz & Maçaira, Paula Medina, 2022. "Validation of the representativeness of wind speed time series obtained from reanalysis data for Brazilian territory," Energy, Elsevier, vol. 258(C).
    3. Yang, Zihao & Dong, Sheng, 2024. "A novel framework for wind energy assessment at multi-time scale based on non-stationary wind speed models: A case study in China," Renewable Energy, Elsevier, vol. 226(C).
    4. Abdelaziz, Sara & Sparrow, Sarah N. & Hua, Weiqi & Wallom, David C.H., 2024. "Assessing long-term future climate change impacts on extreme low wind events for offshore wind turbines in the UK exclusive economic zone," Applied Energy, Elsevier, vol. 354(PB).
    5. Masoud, Alaa A., 2024. "Hybrid wind-solar energy potential modeling using ERA5 and solar irradiation data in google Earth Engine," Renewable Energy, Elsevier, vol. 232(C).
    6. José Rafael Dorrego Portela & Geovanni Hernández Galvez & Quetzalcoatl Hernandez-Escobedo & Ricardo Saldaña Flores & Omar Sarracino Martínez & Orlando Lastres Danguillecourt & Pascual López de Paz & A, 2022. "Microscale Wind Assessment, Comparing Mesoscale Information and Observed Wind Data," Sustainability, MDPI, vol. 14(19), pages 1-12, September.
    7. Hamza S. Abdalla Lagili & Aşkın Kiraz & Youssef Kassem & Hüseyin Gökçekuş, 2023. "Wind and Solar Energy for Sustainable Energy Production for Family Farms in Coastal Agricultural Regions of Libya Using Measured and Multiple Satellite Datasets," Energies, MDPI, vol. 16(18), pages 1-53, September.
    8. Alain Ulazia & Ander Nafarrate & Gabriel Ibarra-Berastegi & Jon Sáenz & Sheila Carreno-Madinabeitia, 2019. "The Consequences of Air Density Variations over Northeastern Scotland for Offshore Wind Energy Potential," Energies, MDPI, vol. 12(13), pages 1-18, July.
    9. Olaofe, Z.O., 2019. "Quantification of the near-surface wind conditions of the African coast: A comparative approach (satellite, NCEP CFSR and WRF-based)," Energy, Elsevier, vol. 189(C).
    10. Katinas, Vladislovas & Gecevicius, Giedrius & Marciukaitis, Mantas, 2018. "An investigation of wind power density distribution at location with low and high wind speeds using statistical model," Applied Energy, Elsevier, vol. 218(C), pages 442-451.
    11. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2014. "Sensitivity of the WRF model wind simulation and wind energy production estimates to planetary boundary layer parameterizations for onshore and offshore areas in the Iberian Peninsula," Applied Energy, Elsevier, vol. 135(C), pages 234-246.
    12. Rabbani, R. & Zeeshan, M., 2020. "Exploring the suitability of MERRA-2 reanalysis data for wind energy estimation, analysis of wind characteristics and energy potential assessment for selected sites in Pakistan," Renewable Energy, Elsevier, vol. 154(C), pages 1240-1251.
    13. Onodera, Hiroaki & Delage, Rémi & Nakata, Toshihiko, 2024. "The role of regional renewable energy integration in electricity decarbonization—A case study of Japan," Applied Energy, Elsevier, vol. 363(C).
    14. He, J.Y. & Li, Q.S. & Chan, P.W. & Zhao, X.D., 2023. "Assessment of future wind resources under climate change using a multi-model and multi-method ensemble approach," Applied Energy, Elsevier, vol. 329(C).
    15. Lins, Davi Ribeiro & Guedes, Kevin Santos & Pitombeira-Neto, Anselmo Ramalho & Rocha, Paulo Alexandre Costa & de Andrade, Carla Freitas, 2023. "Comparison of the performance of different wind speed distribution models applied to onshore and offshore wind speed data in the Northeast Brazil," Energy, Elsevier, vol. 278(PA).
    16. Wen, Yi & Kamranzad, Bahareh & Lin, Pengzhi, 2022. "Joint exploitation potential of offshore wind and wave energy along the south and southeast coasts of China," Energy, Elsevier, vol. 249(C).
    17. Jianxing Yu & Yiqin Fu & Yang Yu & Shibo Wu & Yuanda Wu & Minjie You & Shuai Guo & Mu Li, 2019. "Assessment of Offshore Wind Characteristics and Wind Energy Potential in Bohai Bay, China," Energies, MDPI, vol. 12(15), pages 1-19, July.
    18. Gangopadhyay, Anasuya & Seshadri, Ashwin K. & Patil, Balachandra, 2024. "Wind-solar-storage trade-offs in a decarbonizing electricity system," Applied Energy, Elsevier, vol. 353(PA).
    19. Gil Ruiz, Samuel Andrés & Cañón Barriga, Julio Eduardo & Martínez, J. Alejandro, 2022. "Assessment and validation of wind power potential at convection-permitting resolution for the Caribbean region of Colombia," Energy, Elsevier, vol. 244(PB).
    20. 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).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1108-:d:1598759. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.