IDEAS home Printed from https://ideas.repec.org/a/taf/tjsmxx/v19y2025i3p265-282.html
   My bibliography  Save this article

Advanced simulation-based predictive modelling for solar irradiance sensor farms

Author

Listed:
  • José L. Risco-Martín
  • Ignacio-Iker Prado-Rujas
  • Javier Campoy
  • María S. Pérez
  • Katzalin Olcoz

Abstract

The need for accurate solar power forecasting is critical for grid stability as solar energy becomes more prevalent. This paper presents a new framework called Cloud-based Analysis and Integration for Data Efficiency (CAIDE) for real-time monitoring and forecasting of solar irradiance in sensor farms. CAIDE can handle multiple sensor farms, enhance predictive models in real-time, and is built on Model Based Systems Engineering (MBSE) and Internet of Things (IoT) technologies. It can correct its forecasts, ensuring they stay current, and operates on various architectures, ensuring scalability. Tested on multiple sensor farms, CAIDE proved to be scalable and improved the initial accuracy of solar power production forecasts in real-time. This framework is significant for solar plant deployment and the advancement of renewable energy.

Suggested Citation

  • José L. Risco-Martín & Ignacio-Iker Prado-Rujas & Javier Campoy & María S. Pérez & Katzalin Olcoz, 2025. "Advanced simulation-based predictive modelling for solar irradiance sensor farms," Journal of Simulation, Taylor & Francis Journals, vol. 19(3), pages 265-282, May.
    • Handle: RePEc:taf:tjsmxx:v:19:y:2025:i:3:p:265-282
    DOI: 10.1080/17477778.2024.2333775
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/17477778.2024.2333775
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/17477778.2024.2333775?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

    for a different version of it.

    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:taf:tjsmxx:v:19:y:2025:i:3:p:265-282. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/tjsm .

    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.