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

Shamaila Iram (), Hafiz Muhammad Athar Farid (), Abduljelil Adeola Akande and Hafiz Muhammad Shakeel
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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

Energies, 2025, vol. 18, issue 14, 1-23

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.

Keywords: energy efficiency; residential buildings; feature selection; energy performance; dimensionality reduction; building features (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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