IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v237y2024ipbs0960148124017075.html
   My bibliography  Save this article

One-step ahead short-term hourly global solar radiation forecasting with a dynamical system based on classification of days

Author

Listed:
  • Huang, Jing
  • Yuan, Chengxu
  • Boland, John
  • Guo, Su
  • Liu, Weidong

Abstract

The data-driven method is a common approach to solar irradiation prediction. However, its weakness is that it is highly dependent on the characteristics of the prior data, which leads to a lag delay in forecasting. Therefore, this paper proposes a prediction model that considers data-driven, data-cycle variations, and statistical information for solving such problems. Firstly, the periodicity of the data is removed using the Fourier transform. Secondly, monthly daily characterization matrices are constructed to predict from a global perspective without including information from previous time series. Finally, the coupled autoregressive and dynamical system (CARDS) model is improved using the identity matrix. Comparing the model proposed in this paper with seven other popular prediction models, the error can be reduced by 30.2 % on average at four locations in the northern and southern hemispheres. Moreover, the proposed model does not increase the computational burden of existing models.

Suggested Citation

  • Huang, Jing & Yuan, Chengxu & Boland, John & Guo, Su & Liu, Weidong, 2024. "One-step ahead short-term hourly global solar radiation forecasting with a dynamical system based on classification of days," Renewable Energy, Elsevier, vol. 237(PB).
    • Handle: RePEc:eee:renene:v:237:y:2024:i:pb:s0960148124017075
    DOI: 10.1016/j.renene.2024.121639
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148124017075
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2024.121639?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:renene:v:237:y:2024:i:pb:s0960148124017075. 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/renewable-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.