Test rooms to study human comfort in buildings: A review of controlled experiments and facilities

Pisello, Anna Laura; Pigliautile, Ilaria; Andargie, Maedot S.; Berger, Christiane; Bluyssen, Philomena M.; Carlucci, Salvatore; Chinazzo, Giorgia; Belafi, Zs. Deme; Dong, Bing; Favero, Matteo; Ghahramani, Ali; Havenith, George; Heydarian, Arsalan; Kastner, D.; Kong, Mi Kyung; Licina, Dusan; Liu, Y.; Luna-Navarro, Alessandra; Mahdavi, Ardeshir; Nocente, Alessandro; Schweiker, Marcel; Touchie, Marianne F.; Vellei, Marika; Vittori, F.; Wagner, Andreas; Wang, A.; Wei, Shen

doi:10.1016/j.rser.2021.111359

Cited by 42 publications

(7 citation statements)

References 298 publications

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“…While verifying the reliability of the results obtained from the interpretability analysis by comparing them with the findings of existing studies, we find that many features have dramatically different effects on different samples, which have not received sufficient attention in previous studies. Since field studies cannot directly control the environmental parameters of the investigated space, 49 the cutoff points and various thresholds summarized based on data from field studies around the world are only reference values, which can reflect the overall trend, but the values may change when the environmental variables change. These findings also mean that the dramatic effects of features on thermal sensations are just one of the ways features work, and differentiation is another typical form of effects, and by comparing data analysis methods, we believe that the methods of analyzing only from a global perspective may be an important reason why these significant differential effects are overlooked.…”

Section: Discussionmentioning

confidence: 99%

Interpretability analysis for thermal sensation machine learning models: An exploration based on the SHAP approach

et al. 2022

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Machine learning models have been widely used for studying thermal sensations.However, the black-box properties of machine learning models lead to the lack of model transparency, and existing explanations for the thermal sensation models are generally flawed in terms of the perspectives of interpretable methods. In this study, we perform an interpretability analysis using the "SHapley Additive exPlanation" (SHAP) from game theory for thermal sensation machine learning models. The effects of different features on thermal sensations and typical decision routes in the models are investigated from both local and global perspectives, and the properties of correlation between features and thermal sensations and decision routes within machine learning models are summarized. The differences in the effects of features across samples reflect the effects of features on thermal sensations not only can be demonstrated by significant magnitudes but also by differentiation. The effects of features on thermal sensations often appear in the form of combinations of two to four features, which determine the final thermal sensation in most cases. Therefore, the neutral environment may actually be a dynamic high-dimensional space consisting of certain combinations of features in certain ranges with changing shapes.

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Section: Discussionmentioning

confidence: 99%

Interpretability analysis for thermal sensation machine learning models: An exploration based on the SHAP approach

et al. 2022

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“…Rehman et al [ 15 ] proposed a novel dual-layer PCM configuration for brick walls to maintain human comfort for hot and cold climatic conditions. Pisello et al [ 16 ] reviewed 187 existing test rooms to find out definitions, concepts, and procedures related to human comfort studies. Ganesh et al [ 17 ] reviewed how physical, psychological, personal, and environmental factors affected human thermal comfort, and efforts had been made to simplify a rather complex relationship between comfort parameters, occupant well-being, and IEQ (Indoor environment quality).…”

Section: Introductionmentioning

confidence: 99%

Research on the novel medical protective clothing for COVID-19

Xin¹,

Miao²,

Liu³

2023

Heliyon

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“…In developed countries, people used to spend 85-90% of their time in indoor environments [1][2][3][4]. As a result, around 50% of the building's energy is used to maintain healthy and comfortable indoor thermal conditions [5,6].…”

Section: Introductionmentioning

confidence: 99%

Field Assessment of The Indoor Thermal Environment and Thermal Comfort Levels for Naturally Conditioned Residential Buildings in The Tropical Climate

Al-Absi

Isa²,

Ismail³

2022

ARFMTS

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As we spend most of our time in indoor spaces, half of the energy consumption in buildings is used to improve the indoor thermal environment to achieve thermally comfortable conditions. However, studies showed that people in naturally ventilated buildings have a broader thermal comfort zone. Additionally, people in hot areas can tolerate higher indoor temperatures. Therefore, this work investigates and evaluates the indoor thermal environment and the thermal comfort level in naturally conditioned buildings. Field measurements were conducted in various residential spaces in different buildings. The measurements were used to calculate the operative temperature (Tₒₚ), which was utilized to evaluate the thermal comfort level based on the adaptive models and taking into account the effect of the various orientations. The results showed that maximum Tₒₚ ranged between 27.70 ℃ and 35.50 ℃, depending mainly on the space’s orientation. Additionally, the thermal discomfort time ranged between 0% and 73%. The South & East, Southeast, South, and East orientation were more critical during the study period, while the North, Northeast, and Northwest orientations achieved better indoor thermal conditions. However, these results can vary throughout the year, depending on the sun’s path. This study revealed that the daily average heat gain to the building from the Southeast and South orientations is higher than that from the East and West orientations due to the longer exposure time to solar radiation.

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Test rooms to study human comfort in buildings: A review of controlled experiments and facilities

Cited by 42 publications

References 298 publications

Interpretability analysis for thermal sensation machine learning models: An exploration based on the SHAP approach

Interpretability analysis for thermal sensation machine learning models: An exploration based on the SHAP approach

Research on the novel medical protective clothing for COVID-19

Field Assessment of The Indoor Thermal Environment and Thermal Comfort Levels for Naturally Conditioned Residential Buildings in The Tropical Climate

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