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Development of an optimized simplified model to measure indoor thermal comfort in the built environment.

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Title: Development of an optimized simplified model to measure indoor thermal comfort in the built environment.
Authors: Morresi, Nicole1,2 (AUTHOR) n.morresi@pm.univpm.it, Mazzucchelli, Elisa1 (AUTHOR), Cipollone, Vittoria1 (AUTHOR), Serroni, Serena1 (AUTHOR), Revel, Gian Marco1 (AUTHOR)
Source: Measurement Science & Technology. 2026, Vol. 37 Issue 28, p1-16. 16p.
Subjects: Thermal comfort, Parsimonious models, Intelligent buildings, Environmental monitoring
Abstract: This study presents a methodology for measuring indoor thermal comfort assessment using data measured from environmental sensors and fed into a simplified model. The proposed measurement system enables thermal comfort evaluation using a reduced set of directly measurable mechanical and thermal quantities, namely indoor air temperature, outdoor air temperature, and relative humidity, thereby avoiding the need for full microclimatic instrumentation. To this aim, an optimized simplified predicted mean vote model (sPMVopt) is introduced within a model-based measurement framework. Clothing insulation, which is not directly measurable in operational buildings, is estimated dynamically using a generalized additive model to capture the non-linear relationship between environmental variables and occupants' clothing behavior. The resulting indirect clothing insulation estimates are subsequently approximated by third-degree polynomial functions, yielding an analytical formulation suitable for real-time implementation. Based on the distribution of the estimated insulation values, four refined clothing insulation ranges are identified, and for each range climate-specific regression coefficients are calibrated using data from the American Society of Heating, Refrigerating and Air-Conditioning Engineers Global Thermal Comfort Database II, considering three representative European climate zones (France, Portugal, and Sweden). The proposed measurement system is validated against the reference PMV defined by ISO 7730 and tested in a real office environment. Results show that sPMVopt achieves mean absolute error and mean squared error values of 0.19 and 0.06, respectively, corresponding to improvements of 13%–17% compared to existing reference methodologies (e.g. PMV). A sensitivity analysis also shows that air temperature is the input variable that causes a higher deviation sPMVopt. Overall, the study demonstrates that reliable thermal comfort measurement can be achieved through a simplified sensing setup and a validated model-based estimation process, enabling scalable and real-time comfort monitoring for building diagnostics, HVAC commissioning, and smart-building applications. [ABSTRACT FROM AUTHOR]
Copyright of Measurement Science & Technology is the property of IOP Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 195252347
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PubType: Academic Journal
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  Data: Development of an optimized simplified model to measure indoor thermal comfort in the built environment.
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  Data: <searchLink fieldCode="JN" term="%22Measurement+Science+%26+Technology%22">Measurement Science & Technology</searchLink>. 2026, Vol. 37 Issue 28, p1-16. 16p.
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  Data: This study presents a methodology for measuring indoor thermal comfort assessment using data measured from environmental sensors and fed into a simplified model. The proposed measurement system enables thermal comfort evaluation using a reduced set of directly measurable mechanical and thermal quantities, namely indoor air temperature, outdoor air temperature, and relative humidity, thereby avoiding the need for full microclimatic instrumentation. To this aim, an optimized simplified predicted mean vote model (sPMVopt) is introduced within a model-based measurement framework. Clothing insulation, which is not directly measurable in operational buildings, is estimated dynamically using a generalized additive model to capture the non-linear relationship between environmental variables and occupants' clothing behavior. The resulting indirect clothing insulation estimates are subsequently approximated by third-degree polynomial functions, yielding an analytical formulation suitable for real-time implementation. Based on the distribution of the estimated insulation values, four refined clothing insulation ranges are identified, and for each range climate-specific regression coefficients are calibrated using data from the American Society of Heating, Refrigerating and Air-Conditioning Engineers Global Thermal Comfort Database II, considering three representative European climate zones (France, Portugal, and Sweden). The proposed measurement system is validated against the reference PMV defined by ISO 7730 and tested in a real office environment. Results show that sPMVopt achieves mean absolute error and mean squared error values of 0.19 and 0.06, respectively, corresponding to improvements of 13%–17% compared to existing reference methodologies (e.g. PMV). A sensitivity analysis also shows that air temperature is the input variable that causes a higher deviation sPMVopt. Overall, the study demonstrates that reliable thermal comfort measurement can be achieved through a simplified sensing setup and a validated model-based estimation process, enabling scalable and real-time comfort monitoring for building diagnostics, HVAC commissioning, and smart-building applications. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Measurement Science & Technology is the property of IOP Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1088/1361-6501/ae8290
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        Text: English
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              M: 07
              Text: 2026
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