Machine Learning-Based Predictive Models for Indoor Air Quality and Thermal Comfort: Bridging Sensor Data and Human Perception in Healthcare Facilities

Razak, Tajul Rosli; Ismail, Mohammad Hafiz; Jarimi, Hasila; Nadzir, Mohd Shahrul Mohd; Zheng, Tianhong; Yanan, Zhang; Ahmad, Emy Zairah; Syafiq, Ubaidah; Ludin, Norasikin Ahmad; Rahman, Noor Muhamad Abd.; Jamaludin, Mohd Haikal; Riffat, Saffa

doi:10.61552/EC.2025.003

Energy Catalyst

Volume 1, 2025, Article 2025.003

Technical Article

Machine Learning-Based Predictive Models for Indoor Air Quality and Thermal Comfort: Bridging Sensor Data and Human Perception in Healthcare Facilities

, , , , , , , , , , ,

¹Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 40450 Shah Alam, Malaysia; Department of Architecture and Built Environment, The University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom

²Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, 02600 Arau, Malaysia

³Solar Energy Research Institute. The National University of Malaysia, 43600 Bangi Selangor, Malaysia; Department of Architecture and Built Environment, The University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom

⁴Department of Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia

⁵Department of Architecture and Built Environment, The University of Nottingham, University Park, Nottingham, NG7 2RD, United Kingdom

⁶Faculty of Electrical Technology and Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal Melaka, Malaysia

⁷Solar Energy Research Institute. The National University of Malaysia, 43600 Bangi Selangor, Malaysia

⁸Engineering Services Division, Ministry of Health Malaysia, 62590, Putrajaya, Malaysia

Abstract

Poor indoor air quality (IAQ) and inadequate thermal comfort continue to challenge healthcare facilities in Malaysia, especially in rural areas where poor ventilation and high humidity are prevalent. These environmental stressors negatively impact patient outcomes, staff well-being, and operational efficiency. Existing solutions often overlook the integration of subjective human perception with objective sensor data, resulting in limited adaptability and responsiveness. This study introduces a datadriven, human-centric framework that combines environmental sensor measurements with user-reported comfort feedback to develop predictive models for IAQ and thermal comfort. A comprehensive machine learning pipeline was implemented using four algorithms—Random Forest (RF), XGBoost, Artificial Neural Network (ANN), and Support Vector Machine (SVM)—to model both continuous IAQ indicators and categorical thermal preferences. Experimental results show that Random Forest achieved the best overall performance, with the lowest root mean squared error (RMSE = 14.35) in regression and the highest classification accuracy (87.5%) in predicting thermal preference. Statistical validation confirmed that Random Forest and XGBoost performed similarly in regression, while Random Forest and SVM showed no significant differences in classification accuracy. These findings validate Random Forest as a robust and consistent model across both tasks. This study contributes a validated, AI-enhanced framework for intelligent environmental monitoring in healthcare settings, emphasizing the integration of subjective and objective data streams. The approach supports personalized, data-driven interventions and offers practical insights for facility managers, clinicians, and policymakers aiming to optimize indoor conditions. Future work will focus on scaling the dataset across diverse facilities and climates, enabling real-time deployment, and incorporating explainable AI techniques to enhance model transparency and stakeholder trust.

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Machine Learning-Based Predictive Models for Indoor Air Quality and Thermal Comfort: Bridging Sensor Data and Human Perception in Healthcare Facilities

Abstract

References

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