IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i14p3878-d1706317.html
   My bibliography  Save this article

Intelligent Modelling Techniques for Enhanced Thermal Comfort and Energy Optimisation in Residential Buildings

Author

Listed:
  • Shamaila Iram

    (Department of Computer Science, University of Huddersfield, Huddersfield HD1 3DH, UK)

  • Hafiz Muhammad Athar Farid

    (Department of Computer Science, University of Huddersfield, Huddersfield HD1 3DH, UK)

  • Abduljelil Adeola Akande

    (Department of Computer Science, University of Huddersfield, Huddersfield HD1 3DH, UK)

  • Hafiz Muhammad Shakeel

    (Department of Computer Science, University of Huddersfield, Huddersfield HD1 3DH, UK)

Abstract

This study examines the utilisation of sophisticated predictive methodologies to enhance the energy efficiency and comfort of residential structures. The ASHRAE Global Thermal Comfort Database II was employed to construct and evaluate machine learning models that were designed to predict thermal comfort levels while optimising energy consumption. Air temperature, garment insulation, metabolic rate, air velocity, and humidity were identified as critical comfort determinants. Numerous predictive models were assessed, and XGBoost demonstrated improved performance as a result of hyperparameter optimisation (R 2 = 0.9394, MSE = 0.0224). The study underscores the ability of sophisticated algorithms to clarify the complex relationships between environmental factors and occupant comfort. This sophisticated modelling methodology provides a practical approach to enhancing the efficiency of residential energy consumption while simultaneously ensuring the comfort of the occupants, thereby promoting more sustainable and comfortable living environments.

Suggested Citation

  • Shamaila Iram & Hafiz Muhammad Athar Farid & Abduljelil Adeola Akande & Hafiz Muhammad Shakeel, 2025. "Intelligent Modelling Techniques for Enhanced Thermal Comfort and Energy Optimisation in Residential Buildings," Energies, MDPI, vol. 18(14), pages 1-23, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:14:p:3878-:d:1706317
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/14/3878/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/14/3878/
    Download Restriction: no
    ---><---

    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:gam:jeners:v:18:y:2025:i:14:p:3878-:d:1706317. 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.