IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/9710839.html
   My bibliography  Save this article

Using the Cloud-Bayesian Network in Environmental Assessment of Offshore Wind-Farm Siting

Author

Listed:
  • Ming Li
  • Kefeng Liu
  • Ren Zhang
  • Mei Hong
  • Qin Pan

Abstract

Offshore wind energy has become the fastest growing form of renewable energy for the last few years. And the development of offshore wind farms (OWFs) is now characterized by a boom. OWF siting is crucial in the success of wind energy projects. Therefore, this paper aims to introduce intelligent algorithms to improve the siting assessment under conditions of multisource and uncertain information. An optimization macrositing model based on Cloud-Bayesian Network (Cloud-BN) is put forward. We introduce the cloud model and adaptive Gaussian cloud transformation (A-GCT) algorithm to grade indicators and apply BN to achieve nonlinear integration and inference of multi-indicators. Combined with the fuzzy representation of the cloud model and probabilistic reasoning of BN, the proposed model can investigate the most efficient siting areas of OWFs in the North Sea of Europe. The experimental results indicate that the siting accuracy is up to % with reference to the actual OWF location.

Suggested Citation

  • Ming Li & Kefeng Liu & Ren Zhang & Mei Hong & Qin Pan, 2019. "Using the Cloud-Bayesian Network in Environmental Assessment of Offshore Wind-Farm Siting," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-15, July.
    • Handle: RePEc:hin:jnlmpe:9710839
    DOI: 10.1155/2019/9710839
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2019/9710839.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/MPE/2019/9710839.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2019/9710839?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wimhurst, Joshua J. & Greene, J. Scott & Koch, Jennifer, 2023. "Predicting commercial wind farm site suitability in the conterminous United States using a logistic regression model," Applied Energy, Elsevier, vol. 352(C).

    More about this item

    Statistics

    Access and download statistics

    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:hin:jnlmpe:9710839. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.