{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:41:18Z","timestamp":1760060478964,"version":"build-2065373602"},"reference-count":58,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T00:00:00Z","timestamp":1756771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100019370","name":"the Foundation for Science and Technology","doi-asserted-by":"publisher","award":["COMPETE2030-FEDER-00848800"],"award-info":[{"award-number":["COMPETE2030-FEDER-00848800"]}],"id":[{"id":"10.13039\/501100019370","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Energies"],"abstract":"<jats:p>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\u2014multiple linear regression (MLR)\u2014to predict the dynamic performance of an office building\u2019s 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\u2014one in September and one in December\u2014but 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 \u00b0C 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 \u00b0C, 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.<\/jats:p>","DOI":"10.3390\/en18174656","type":"journal-article","created":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T15:02:57Z","timestamp":1756825377000},"page":"4656","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Behavior of a Glazing System and Its Impact on Thermal Comfort: Short-Term In Situ Assessment and Machine Learning-Based Predictive Modeling"],"prefix":"10.3390","volume":"18","author":[{"given":"Saman Abolghasemi","family":"Moghaddam","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, University Coimbra, Rua Lu\u00eds Reis Santos, P\u00f3lo II, 3030-788 Coimbra, Portugal"},{"name":"Itecons\u2014Institute for Research and Technological Development in Construction, Energy, Environment and Sustainability, Rua Pedro Hispano, 3030-289 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3418-0030","authenticated-orcid":false,"given":"Nuno","family":"Sim\u00f5es","sequence":"additional","affiliation":[{"name":"Itecons\u2014Institute for Research and Technological Development in Construction, Energy, Environment and Sustainability, Rua Pedro Hispano, 3030-289 Coimbra, Portugal"},{"name":"CERIS, Department of Civil Engineering, University Coimbra, Rua Lu\u00eds Reis Santos, P\u00f3lo II, 3030-790 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6332-1755","authenticated-orcid":false,"given":"Michael","family":"Brett","sequence":"additional","affiliation":[{"name":"Itecons\u2014Institute for Research and Technological Development in Construction, Energy, Environment and Sustainability, Rua Pedro Hispano, 3030-289 Coimbra, Portugal"},{"name":"CERIS, Department of Civil Engineering, University Coimbra, Rua Lu\u00eds Reis Santos, P\u00f3lo II, 3030-790 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0739-9811","authenticated-orcid":false,"given":"Manuel Gameiro","family":"da Silva","sequence":"additional","affiliation":[{"name":"ADAI, Department of Mechanical Engineering, University Coimbra, Rua Lu\u00eds Reis Santos, P\u00f3lo II, 3030-788 Coimbra, Portugal"}]},{"given":"Joana","family":"Prata","sequence":"additional","affiliation":[{"name":"Itecons\u2014Institute for Research and Technological Development in Construction, Energy, Environment and Sustainability, Rua Pedro Hispano, 3030-289 Coimbra, Portugal"},{"name":"CERIS, Department of Civil Engineering, University Coimbra, Rua Lu\u00eds Reis Santos, P\u00f3lo II, 3030-790 Coimbra, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111711","DOI":"10.1016\/j.jobe.2024.111711","article-title":"Advanced hybrid assessment of glazing systems: A comparative analysis using differential and global methods for precise thermal optimization and energy efficiency in building","volume":"100","author":"Salazar","year":"2025","journal-title":"J. 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