IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v331y2025ics036054422502701x.html
   My bibliography  Save this article

Towards sustainable energy efficiency: Data-driven optimization in large-scale plants using machine learning applications

Author

Listed:
  • Ha, Byeongmin
  • Lee, Hyeonjeong
  • Hwangbo, Soonho

Abstract

This study presents a machine learning–based optimization framework for utility systems in large-scale manufacturing operations. Designed for broad applicability across diverse industrial processes, the framework integrates historical operational and utility data to support energy-efficient decision-making. Three case studies were conducted to evaluate the effectiveness of the framework. The first case involved identifying feasible operating regions from high-resolution data to optimize utility production in a plant-level utility system. Through this, utility consumption was reduced by 2 %–11 %, resulting in economic efficiency improvements ranging from 6 % to 10 %. The associated reductions in greenhouse gas emissions were also estimated using a life cycle assessment database. The second case applied representation learning techniques to evaluate the optimality of current process operations by comparing them with similar historical instances, offering operational guidance based on data-driven similarity analysis. The third case focused on data storage optimization, where transformation of industrial datasets led to approximately 140-fold reduction in data volume, with implications for integration with image-based AI systems. Together, these case studies demonstrate the potential of machine learning techniques to reduce energy usage, enhance economic performance, and improve data handling in complex manufacturing environments.

Suggested Citation

  • Ha, Byeongmin & Lee, Hyeonjeong & Hwangbo, Soonho, 2025. "Towards sustainable energy efficiency: Data-driven optimization in large-scale plants using machine learning applications," Energy, Elsevier, vol. 331(C).
  • Handle: RePEc:eee:energy:v:331:y:2025:i:c:s036054422502701x
    DOI: 10.1016/j.energy.2025.137059
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422502701X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2025.137059?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

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:eee:energy:v:331:y:2025:i:c:s036054422502701x. 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.