Thermal history and adaptation: Does a long-term indoor thermal exposure impact human thermal adaptability?

Ning, Haoran; Wang, Zhaojun; Ji, Yuchen

doi:10.1016/j.apenergy.2016.08.157

Cited by 84 publications

(31 citation statements)

References 36 publications

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“…Several static and dynamic factors that influence occupants' satisfaction with their thermal environment are not used by these two models. Static factors (e.g., race, gender; Karjalainen, 2012) are independent of time while dynamic factors (e.g., acclimation, age, and food intake; Brager and de Dear, 1998;Schellen et al, 2010;Ugursal and Culp, 2013;Ning et al, 2016) contribute to the change in thermal comfort over time.…”

Section: Thermal Comfort Research Air Conditioning and Personal Commentioning

confidence: 99%

Artificial Intelligence for Efficient Thermal Comfort Systems: Requirements, Current Applications and Future Directions

Ghahramani

Galicia

Lehrer

et al. 2020

Front. Built Environ.

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In buildings, one or a combination of systems (e.g., central HVAC system, ceiling fan, desk fan, personal heater, and foot warmer) are often responsible for providing thermal comfort to the occupants. While thermal comfort has been shown to differ from person to person and vary over time, these systems are often operated based on prefixed setpoints and schedule of operations or at the request/routine of each individual. This leads to occupants' discomfort and energy wastes. To enable the improvements in both comfort and energy efficiency autonomously, in this paper, we describe the necessity of an integrated system of sensors (e.g., wearable sensors/infrared sensors), infrastructure for enabling system interoperability, learning and control algorithms, and actuators (e.g., HVAC system setpoints, ceiling fans) to work under a governing central intelligent system. To assist readers with little to no exposure to artificial intelligence (AI), we describe the fundamentals of an intelligent entity (rational agent) and components of its problem-solving process (i.e., search algorithms, logic inference, and machine learning) and provide examples from the literature. We then discuss the current application of intelligent personal thermal comfort systems in buildings based on a comprehensive review of the literature. We finally describe future directions for enabling application of fully automated systems to provide comfort in an efficient manner. It is apparent that improvements in all aspects of an intelligent system are be needed to better ascertain the correct combination of systems to activate and for how long to increase the overall efficiency of the system and improve comfort.

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Section: Thermal Comfort Research Air Conditioning and Personal Commentioning

confidence: 99%

Artificial Intelligence for Efficient Thermal Comfort Systems: Requirements, Current Applications and Future Directions

Ghahramani

Galicia

Lehrer

et al. 2020

Front. Built Environ.

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“…The standard deviations of the results of the thermal-manikin experiments for clothing ensembles were very small: 0.003 clo. In previous studies [35,36], occupants wore clothing of <1.0 clo during the winter. Therefore, in the present study, experiments were performed with a clothing ensemble having a relatively low clo level (<1.0 clo).…”

Section: Evaluation Of Clo Level For Clothes Used In Experiments Accormentioning

confidence: 99%

Assessment of a Real-Time Prediction Method for High Clothing Thermal Insulation Using a Thermoregulation Model and an Infrared Camera

Lee¹,

Choi

Kim

et al. 2020

Atmosphere

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For evaluating the thermal comfort of occupants, human factors such as clothing thermal insulation (clo level) and metabolic rate (Met) are one of the important parameters as well as environmental factors such as air temperature (Ta) and humidity. In general, a fixed clo level is commonly used for controlling heating, ventilation, and air conditioning using the thermal comfort index. However, a fixed clo level can lead to errors for estimating the thermal comfort of occupants, because clo levels of occupants can vary with time and by season. The present study assesses a method for predicting the clo level of occupants using a thermoregulation model and an infrared (IR) camera. The Tanabe model and the Fanger model were used as the thermoregulation models, and the predicted performance for high clo level (winter clothing) was compared. The skin and clothing temperatures of eight subjects using a non-contact IR camera were measured in a climate chamber. In addition, the measured values were used for the thermoregulation models to predict the clo levels. As a result, the Tanabe model showed a better performance than the Fanger model for predicting clo levels. In addition, all models tended to predict a clo level higher than the traditional method.

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“…The contrary phenomenon is also seen in cold tests. The influences caused by short-term or long-term thermal history on thermal perception have been noticed and reported by Luo [41], Ning [42] and Palma [43]. Hence, one more factor is needed to explain the bias originated from the dynamic process.…”

Section: Figure 12mentioning

confidence: 99%

Chinese older people's subjective and physiological responses to moderate cold and warm temperature steps

Wang

Jiao

et al. 2019

Building and Environment

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Older people are very likely to experience transitions among spaces with different temperatures in daily life. But little has been known about their thermal comfort and physiological responses to these temperature steps. This study investigated 18 healthy older people's thermal perceptions and physiological parameters under cold and warm exposures with 3/5/6 o C temperature steps. The results showed that subjects' thermal sensation was sensitive to all moderate temperature steps, but their thermal comfort perception could only distinguish temperature changes greater than 5℃. Thermal unacceptability was only observed when subjects' tympanic temperature reached at 37.08℃. Also, we found older people need more than 50 minutes time to get their mean skin temperature steady after cold stimuli, while they only need < 24 minutes after warm ones. Cold stimuli could significantly boost subjects' blood pressure, respiratory rate, blood oxygen saturation, and depress heart rate. To predict older people's transient thermal sensation after temperature steps, we proposed two regression models for cold and warm exposures respectively. Based on the above observations, we suggest older people should try their best to avoid large step temperature changes, especially for cold side steps.

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Thermal history and adaptation: Does a long-term indoor thermal exposure impact human thermal adaptability?

Cited by 84 publications

References 36 publications

Artificial Intelligence for Efficient Thermal Comfort Systems: Requirements, Current Applications and Future Directions

Artificial Intelligence for Efficient Thermal Comfort Systems: Requirements, Current Applications and Future Directions

Assessment of a Real-Time Prediction Method for High Clothing Thermal Insulation Using a Thermoregulation Model and an Infrared Camera

Chinese older people's subjective and physiological responses to moderate cold and warm temperature steps

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