IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v190y2024ipas1364032123009462.html
   My bibliography  Save this article

Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects

Author

Listed:
  • Arafat, M.Y.
  • Hossain, M.J.
  • Alam, Md Morshed

Abstract

Predictive maintenance is an essential aspect of microgrid operations as it enables identifying potential equipment failures in advance, reducing downtime, and increasing the overall efficiency of the system. Machine learning-based techniques have a great potential to be effective in improving the accuracy of failure predictions, detecting, and diagnosing faults in real-time, and monitoring the health and remaining useful life of microgrid components. The integration of these techniques with microgrid components can lead to reduced downtime, improved safety, overall efficiency, and sustainability. This work aims to explore the research scope of machine learning-based predictive maintenance in microgrid systems. The analysis provides a comprehensive review of the state-of-the-art machine learning techniques that could be used for microgrid predictive maintenance and highlights the gaps and challenges that need to be addressed. This study suggests future research directions in the field and frameworks to improve predictive maintenance using machine learning for microgrid industries.

Suggested Citation

  • Arafat, M.Y. & Hossain, M.J. & Alam, Md Morshed, 2024. "Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    • Handle: RePEc:eee:rensus:v:190:y:2024:i:pa:s1364032123009462
    DOI: 10.1016/j.rser.2023.114088
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032123009462
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2023.114088?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:rensus:v:190:y:2024:i:pa:s1364032123009462. 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.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.