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

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  • 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
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    References listed on IDEAS

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