IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v304y2024ics0360544224017870.html
   My bibliography  Save this article

Determining Weibull distribution patterns for wind conditions in building energy-efficient design across the different thermal design zones in China

Author

Listed:
  • Huo, Xujie
  • Yang, Liu
  • Li, Danny H.W.

Abstract

To establish Weibull distribution patterns for outdoor wind speed (WS) over an extended time period and determine wind conditions for building energy-efficient design, this study collected daily WS data from 381 cities spanning all thermal design zones in China. Weibull distribution shape and scale parameters were estimated using three methods: Maximum Likelihood Estimation (MLE), Graphical Method (GM), and Method of Moments (MM). A comprehensive goodness-of-fit assessment of these methods revealed that GM and MLM exhibited superior performance, making them suitable for determining Weibull parameters in the summer, winter, and the entire year. Through a rigorous examination and evaluation of the shape and scale parameters within each subzone across the summer, winter, and the entire year periods, the study identified the Rayleigh distribution as the typical pattern for low WS in building energy-efficient design. The determined Weibull distribution patterns can serve as fundamental information for wind inlet in assessing climate potential, WS in Outdoor Climate Design Conditions, and wind information in Typical Meteorological Year.

Suggested Citation

  • Huo, Xujie & Yang, Liu & Li, Danny H.W., 2024. "Determining Weibull distribution patterns for wind conditions in building energy-efficient design across the different thermal design zones in China," Energy, Elsevier, vol. 304(C).
  • Handle: RePEc:eee:energy:v:304:y:2024:i:c:s0360544224017870
    DOI: 10.1016/j.energy.2024.132013
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224017870
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2024.132013?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Herrero-Novoa, Cristina & Pérez, Isidro A. & Sánchez, M. Luisa & García, Ma Ángeles & Pardo, Nuria & Fernández-Duque, Beatriz, 2017. "Wind speed description and power density in northern Spain," Energy, Elsevier, vol. 138(C), pages 967-976.
    2. Joanna Clarke & Justin Searle, 2021. "Active Building demonstrators for a low-carbon future," Nature Energy, Nature, vol. 6(12), pages 1087-1089, December.
    3. Li, Danny H.W. & Yang, Liu & Lam, Joseph C., 2013. "Zero energy buildings and sustainable development implications – A review," Energy, Elsevier, vol. 54(C), pages 1-10.
    4. Wais, Piotr, 2017. "A review of Weibull functions in wind sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1099-1107.
    5. Liponi, Angelica & Frate, Guido Francesco & Baccioli, Andrea & Ferrari, Lorenzo & Desideri, Umberto, 2022. "Impact of wind speed distribution and management strategy on hydrogen production from wind energy," Energy, Elsevier, vol. 256(C).
    6. Hong, Tianzhen & Chang, Wen-Kuei & Lin, Hung-Wen, 2013. "A fresh look at weather impact on peak electricity demand and energy use of buildings using 30-year actual weather data," Applied Energy, Elsevier, vol. 111(C), pages 333-350.
    7. Shu, Z.R. & Li, Q.S. & Chan, P.W., 2015. "Investigation of offshore wind energy potential in Hong Kong based on Weibull distribution function," Applied Energy, Elsevier, vol. 156(C), pages 362-373.
    8. Dong, Cong & Huang, Guohe (Gordon) & Cheng, Guanhui, 2021. "Offshore wind can power Canada," Energy, Elsevier, vol. 236(C).
    9. Lun, Isaac Y.F & Lam, Joseph C, 2000. "A study of Weibull parameters using long-term wind observations," Renewable Energy, Elsevier, vol. 20(2), pages 145-153.
    10. Liu, Fa & Wang, Xunming & Sun, Fubao & Kleidon, Axel, 2023. "Potential impact of global stilling on wind energy production in China," Energy, Elsevier, vol. 263(PB).
    11. Chang, Tsang-Jung & Wu, Yu-Ting & Hsu, Hua-Yi & Chu, Chia-Ren & Liao, Chun-Min, 2003. "Assessment of wind characteristics and wind turbine characteristics in Taiwan," Renewable Energy, Elsevier, vol. 28(6), pages 851-871.
    12. 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).
    13. Wang, Jianzhou & Huang, Xiaojia & Li, Qiwei & Ma, Xuejiao, 2018. "Comparison of seven methods for determining the optimal statistical distribution parameters: A case study of wind energy assessment in the large-scale wind farms of China," Energy, Elsevier, vol. 164(C), pages 432-448.
    14. He, Junyi & Chan, P.W. & Li, Qiusheng & Lee, C.W., 2020. "Spatiotemporal analysis of offshore wind field characteristics and energy potential in Hong Kong," Energy, Elsevier, vol. 201(C).
    15. Mohammadzadeh Bina, Saeid & Jalilinasrabady, Saeid & Fujii, Hikari & Farabi-Asl, Hadi, 2018. "A comprehensive approach for wind power plant potential assessment, application to northwestern Iran," Energy, Elsevier, vol. 164(C), pages 344-358.
    16. Yang, Liu & Wan, Kevin K.W. & Li, Danny H.W. & Lam, Joseph C., 2011. "A new method to develop typical weather years in different climates for building energy use studies," Energy, Elsevier, vol. 36(10), pages 6121-6129.
    17. Lee, Wen-Shing & Kung, Chung-Kuan, 2011. "Using climate classification to evaluate building energy performance," Energy, Elsevier, vol. 36(3), pages 1797-1801.
    18. Lam, Joseph C. & Tang, H.L. & Li, Danny H.W., 2008. "Seasonal variations in residential and commercial sector electricity consumption in Hong Kong," Energy, Elsevier, vol. 33(3), pages 513-523.
    19. Chang, Tian Pau, 2011. "Estimation of wind energy potential using different probability density functions," Applied Energy, Elsevier, vol. 88(5), pages 1848-1856, May.
    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. 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).
    2. He, J.Y. & Chan, P.W. & Li, Q.S. & Lee, C.W., 2022. "Characterizing coastal wind energy resources based on sodar and microwave radiometer observations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    3. Li, Honglian & Huang, Jin & Hu, Yao & Wang, Shangyu & Liu, Jing & Yang, Liu, 2021. "A new TMY generation method based on the entropy-based TOPSIS theory for different climatic zones in China," Energy, Elsevier, vol. 231(C).
    4. 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.
    5. Dongbum Kang & Kyungnam Ko & Jongchul Huh, 2018. "Comparative Study of Different Methods for Estimating Weibull Parameters: A Case Study on Jeju Island, South Korea," Energies, MDPI, vol. 11(2), pages 1-19, February.
    6. Alkhalidi, Mohamad A. & Al-Dabbous, Shoug Kh. & Neelamani, S. & Aldashti, Hassan A., 2019. "Wind energy potential at coastal and offshore locations in the state of Kuwait," Renewable Energy, Elsevier, vol. 135(C), pages 529-539.
    7. Liu, Long & Zhao, Jing & Liu, Xin & Wang, Zhaoxia, 2014. "Energy consumption comparison analysis of high energy efficiency office buildings in typical climate zones of China and U.S. based on correction model," Energy, Elsevier, vol. 65(C), pages 221-232.
    8. Jung, Christopher & Schindler, Dirk, 2019. "Wind speed distribution selection – A review of recent development and progress," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    9. Fazelpour, Farivar & Markarian, Elin & Soltani, Nima, 2017. "Wind energy potential and economic assessment of four locations in Sistan and Balouchestan province in Iran," Renewable Energy, Elsevier, vol. 109(C), pages 646-667.
    10. Ayman Al-Quraan & Bashar Al-Mhairat, 2022. "Intelligent Optimized Wind Turbine Cost Analysis for Different Wind Sites in Jordan," Sustainability, MDPI, vol. 14(5), pages 1-24, March.
    11. Mazhar H. Baloch & Safdar A. Abro & Ghulam Sarwar Kaloi & Nayyar H. Mirjat & Sohaib Tahir & M. Haroon Nadeem & Mehr Gul & Zubair A. Memon & Mahendar Kumar, 2017. "A Research on Electricity Generation from Wind Corridors of Pakistan (Two Provinces): A Technical Proposal for Remote Zones," Sustainability, MDPI, vol. 9(9), pages 1-31, September.
    12. Pikas, Ergo & Thalfeldt, Martin & Kurnitski, Jarek & Liias, Roode, 2015. "Extra cost analyses of two apartment buildings for achieving nearly zero and low energy buildings," Energy, Elsevier, vol. 84(C), pages 623-633.
    13. He, J.Y. & Chan, P.W. & Li, Q.S. & Huang, Tao & Yim, Steve Hung Lam, 2024. "Assessment of urban wind energy resource in Hong Kong based on multi-instrument observations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    14. Islam, M.R. & Saidur, R. & Rahim, N.A., 2011. "Assessment of wind energy potentiality at Kudat and Labuan, Malaysia using Weibull distribution function," Energy, Elsevier, vol. 36(2), pages 985-992.
    15. Andrés Ruiz & Florin Onea & Eugen Rusu, 2020. "Study Concerning the Expected Dynamics of the Wind Energy Resources in the Iberian Nearshore," Energies, MDPI, vol. 13(18), pages 1-25, September.
    16. Chang, Tian-Pau & Ko, Hong-Hsi & Liu, Feng-Jiao & Chen, Pai-Hsun & Chang, Ying-Pin & Liang, Ying-Hsin & Jang, Horng-Yuan & Lin, Tsung-Chi & Chen, Yi-Hwa, 2012. "Fractal dimension of wind speed time series," Applied Energy, Elsevier, vol. 93(C), pages 742-749.
    17. Wang, Jianzhou & Qin, Shanshan & Jin, Shiqiang & Wu, Jie, 2015. "Estimation methods review and analysis of offshore extreme wind speeds and wind energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 26-42.
    18. Deep, Sneh & Sarkar, Arnab & Ghawat, Mayur & Rajak, Manoj Kumar, 2020. "Estimation of the wind energy potential for coastal locations in India using the Weibull model," Renewable Energy, Elsevier, vol. 161(C), pages 319-339.
    19. César Henrique Mattos Pires & Felipe M. Pimenta & Carla A. D'Aquino & Osvaldo R. Saavedra & Xuerui Mao & Arcilan T. Assireu, 2020. "Coastal Wind Power in Southern Santa Catarina, Brazil," Energies, MDPI, vol. 13(19), pages 1-23, October.
    20. Liu, Fa & Sun, Fubao & Wang, Xunming, 2023. "Impact of turbine technology on wind energy potential and CO2 emission reduction under different wind resource conditions in China," Applied Energy, Elsevier, vol. 348(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:eee:energy:v:304:y:2024:i:c:s0360544224017870. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.