IDEAS home Printed from https://ideas.repec.org/a/ids/ijgeni/v42y2020i3-4p198-211.html
   My bibliography  Save this article

Multi-load prediction modelling for combined energy system of intelligent city buildings

Author

Listed:
  • Huaqing Zeng

Abstract

Aiming at the problem that there is a big difference between the predicted load and the actual load, a multi-load forecasting model for the combined energy system of intelligent city buildings is proposed. Using DeST to construct the building model and simulate the load characteristics of the building, the composite model of the building is constructed and the structure of the energy system is defined. Based on Bayesian theory, the multi-load model of building energy system is constructed. MCMCMC algorithm is used to optimise the multi-load model of building composite energy system, realise multi-load prediction and promote the development of energy industry. The experimental results show that the power load prediction and heat load prediction of this method are always higher than those of other methods, and MAPE is lower than 0.2, which proves that this method can accurately predict the multi-load of buildings with high prediction accuracy.

Suggested Citation

  • Huaqing Zeng, 2020. "Multi-load prediction modelling for combined energy system of intelligent city buildings," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 42(3/4), pages 198-211.
  • Handle: RePEc:ids:ijgeni:v:42:y:2020:i:3/4:p:198-211
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=108957
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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:ids:ijgeni:v:42:y:2020:i:3/4:p:198-211. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=13 .

    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.