Real-time data fusion for thermal comfort prediction using transformer models

Bibars Amangeldy (), Nurdaulet Tasmurzayev (), Baglan Imanbek (), Serik Aibagarov () and Zukhra Abdiakhmetova ()

International Journal of Innovative Research and Scientific Studies, 2025, vol. 8, issue 5, 2370-2380

Abstract: Maintaining optimal thermal comfort in buildings is critical for occupant well-being and energy efficiency. This study introduces a permutation-importance-based feature selection method for multiclass thermal comfort classification using ensemble algorithms. Data comprising environmental (air temperature, humidity, CO₂ concentration) and physiological (SpO₂, blood pressure, BMI, HRV metrics) variables were collected from 1536 samples and labeled on the ASHRAE 7-point scale. Decision Tree, AdaBoost, and CatBoost models were trained on the full feature set, then re-evaluated using only the ten most informative predictors identified via permutation importance. Results show that reduced-feature models match or slightly outperform full-feature counterparts: CatBoost accuracy improved from 0.961 to 0.971, AdaBoost from 0.841 to 0.857, and Decision Tree from 0.688 to 0.825, while dramatically lowering sensor and computational requirements. Paired t-tests confirmed no significant performance loss. This streamlined approach enables cost-effective, real-time thermal comfort monitoring and supports the deployment of intelligent HVAC systems with minimal hardware.

Keywords: CatBoost; Feature selection; Permutation importance; Sensor reduction; Thermal comfort. (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:aac:ijirss:v:8:y:2025:i:5:p:2370-2380:id:9474

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