IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v133y2017icp9-22.html
   My bibliography  Save this article

Urban long term electricity demand forecast method based on system dynamics of the new economic normal: The case of Tianjin

Author

Listed:
  • He, Yongxiu
  • Jiao, Jie
  • Chen, Qian
  • Ge, Sifan
  • Chang, Yan
  • Xu, Yang

Abstract

The development of the new economic normal is influencing the total economy, the industrial structure and the layout in China to a great extent. The pillar industry faces a transformation from factor-driven to innovation-driven. The traditional electricity demand prediction model is no longer applicable due to these new factors. Thus, this paper makes a quantitative analysis of the new influencing factors, and establishes the quantitative relationship based on econometrics. According to industry division, this paper proposes a long term electricity demand forecasting model that is suitable for the new economic normal by using system dynamics method. Finally, the paper combines the actual situation of Tianjin and the forecasting model to predict the electricity demand of Tianjin. Simultaneously, the paper makes some suggestions about the development of the grid company.

Suggested Citation

  • He, Yongxiu & Jiao, Jie & Chen, Qian & Ge, Sifan & Chang, Yan & Xu, Yang, 2017. "Urban long term electricity demand forecast method based on system dynamics of the new economic normal: The case of Tianjin," Energy, Elsevier, vol. 133(C), pages 9-22.
  • Handle: RePEc:eee:energy:v:133:y:2017:i:c:p:9-22
    DOI: 10.1016/j.energy.2017.05.107
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217308630
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2017.05.107?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.

    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:133:y:2017:i:c:p:9-22. 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: 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.