Dynamic Behavior of a Glazing System and Its Impact on Thermal Comfort: Short-Term In Situ Assessment and Machine Learning-Based Predictive Modeling

Saman Abolghasemi Moghaddam (), Nuno Simões (), Michael Brett, Manuel Gameiro da Silva and Joana Prata
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Saman Abolghasemi Moghaddam: Department of Mechanical Engineering, University Coimbra, Rua Luís Reis Santos, Pólo II, 3030-788 Coimbra, Portugal
Nuno Simões: Itecons—Institute for Research and Technological Development in Construction, Energy, Environment and Sustainability, Rua Pedro Hispano, 3030-289 Coimbra, Portugal
Michael Brett: Itecons—Institute for Research and Technological Development in Construction, Energy, Environment and Sustainability, Rua Pedro Hispano, 3030-289 Coimbra, Portugal
Manuel Gameiro da Silva: ADAI, Department of Mechanical Engineering, University Coimbra, Rua Luís Reis Santos, Pólo II, 3030-788 Coimbra, Portugal
Joana Prata: Itecons—Institute for Research and Technological Development in Construction, Energy, Environment and Sustainability, Rua Pedro Hispano, 3030-289 Coimbra, Portugal

Energies, 2025, vol. 18, issue 17, 1-22

Abstract: In the context of retrofitting existing buildings into nearly zero-energy buildings (NZEBs), in situ assessment methods have proven reliable for evaluating the performance of building components, including glazing systems. However, these methods are often time-consuming, intrusive to occupants, and disruptive to building operations. This study investigates the potential of a machine learning approach—multiple linear regression (MLR)—to predict the dynamic performance of an office building’s glazing system by analyzing surface temperature variations and their impact on nearby thermal comfort. The models were trained using in situ data collected over just two weeks—one in September and one in December—but were applied to predict the glazing performance on multiple other dates with diverse weather conditions. Results show that MLR predictions closely matched nighttime measurements, while some discrepancies occurred during the daytime. Nevertheless, the machine learning model achieved a daytime prediction accuracy of approximately 1.5 °C in terms of root mean square error (RMSE), which is lower than the values reported in previous studies. For thermal comfort evaluation, the MLR model identified the periods with thermal discomfort with an overall accuracy of approximately 92%. However, during periods when the difference between predicted and measured operative temperatures exceeded 1 °C, the thermal comfort predictions showed greater deviation from actual measurements. The study concludes by acknowledging its limitations and recommending a future approach that integrates machine learning with laboratory-based techniques (e.g., hot-box setups and solar simulators) and in situ measurements, together with a broader variety of glazing samples, to more effectively evaluate and enhance prediction accuracy, robustness, and generalizability.

Keywords: glazing systems; in-situ methods; dynamic analysis; thermal comfort; machine learning (ML) (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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