IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i8p1860-d1375085.html
   My bibliography  Save this article

Cognitive Systems for the Energy Efficiency Industry

Author

Listed:
  • Javier Arevalo

    (Department of Mechanical Engineering, Public University of Navarra, Av de Tarazona s/n, 31500 Tudela, Navarra, Spain)

  • Juan-Ignacio Latorre-Biel

    (Department of Mechanical Engineering, Public University of Navarra, Av de Tarazona s/n, 31500 Tudela, Navarra, Spain)

  • Francisco-Javier Flor-Montalvo

    (Department of Mechanical Engineering, Public University of Navarra, Av de Tarazona s/n, 31500 Tudela, Navarra, Spain)

  • Mercedes Perez-Parte

    (Department of Mechanical Engineering, University of La Rioja, 26004 Logroño, La Rioja, Spain)

  • Julio Blanco

    (Department of Mechanical Engineering, University of La Rioja, 26004 Logroño, La Rioja, Spain)

Abstract

This review underscores the pivotal role of Cognitive Systems (CS) in enhancing energy efficiency within the industrial sector, exploring the application of sophisticated algorithms, data analytics, and machine learning techniques to the real-time optimization of energy consumption. This methodology has the potential to reduce operational expenses and further diminish environmental repercussions; however, it also leverages data-driven insights and predictive maintenance to foresee equipment malfunctions and modulate energy utilization accordingly. The viability of integrating renewable energy sources is emphasized, supporting a transition towards sustainability. Furthermore, this research includes a bibliometric literature analysis from the past decade on the deployment of CS and Artificial Intelligence in enhancing industrial energy efficiency.

Suggested Citation

  • Javier Arevalo & Juan-Ignacio Latorre-Biel & Francisco-Javier Flor-Montalvo & Mercedes Perez-Parte & Julio Blanco, 2024. "Cognitive Systems for the Energy Efficiency Industry," Energies, MDPI, vol. 17(8), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:8:p:1860-:d:1375085
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/8/1860/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/8/1860/
    Download Restriction: no
    ---><---

    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:gam:jeners:v:17:y:2024:i:8:p:1860-:d:1375085. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.