Field study on adaptive comfort in air conditioned dormitories of university with hot-humid climate in summer

He, Yutong; Li, Nianping; Peng, Jinqing; Zhang, Wenjie; Li, Yiru

doi:10.1016/j.enbuild.2016.03.020

Cited by 62 publications

(35 citation statements)

References 34 publications

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“…As could be seen from Also, to confirm comfort temperature, the preferred temperature was also analyzed in some previous investigations [22,39,43,47,49]. Comparison between the preferred…”

Section: The Neutral Temperature and Preferred Temperaturementioning

confidence: 99%

Field study on adaptive thermal comfort in typical air conditioned classrooms

Fang

Zhang

Cheng

et al. 2018

Building and Environment

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“…As could be seen from Also, to confirm comfort temperature, the preferred temperature was also analyzed in some previous investigations [22,39,43,47,49]. Comparison between the preferred…”

Section: The Neutral Temperature and Preferred Temperaturementioning

confidence: 99%

Field study on adaptive thermal comfort in typical air conditioned classrooms

Fang

Zhang

Cheng

et al. 2018

Building and Environment

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“…The high dependence means the information reflected by air movement preference and acceptability could be explained and 2 PMV is calculated from six measured or estimated physical parameters -air temperature, radiation temperature, humidity, air speed, metabolic rate, and clothing. PMV might also be influenced by psychophysiology factors because psychophysiology factors, such as adaptive behaviors, might influence metabolic rate and clothing [34].…”

Section: Principal Component Analysismentioning

confidence: 99%

Dimension analysis of subjective thermal comfort metrics based on ASHRAE Global Thermal Comfort Database using machine learning

Wang

Wang²,

et al. 2020

Journal of Building Engineering

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We analyzed the ASHRAE Global Thermal Comfort Database II to answer a fundamental but overlooked question in thermal comfort studies: how many and which subjective metrics should be used for the assessment of the occupants' thermal experience. We found that the thermal sensation is the most frequently used metrics in Thermal Comfort Database II, followed by thermal preference, comfort and acceptability. The thermal sensation/thermal preference, thermal comfort/air movement acceptability and thermal comfort/thermal preference are the top three most dependent metrics pairs. A principal component analysis confirmed that the personal experience of thermal conditions in built environment is not a one-dimensional problem, but at least a two-dimensional problem, and suggested thermal sensation and thermal comfort should be asked in right-now surveys as the first two Principal Component are majorly constructed by thermal sensation and thermal comfort. To further confirm the predictive power of thermal sensation and comfort, we used logistic regression and support vector machine to predict thermal acceptability and thermal preference with thermal sensation and comfort. The prediction accuracy is 87% for thermal acceptability and 64% for thermal preference. The prediction error might be due to occupants' individual difference and people errors in answering survey. These findings could help the design of chamber experiments, field studies, and human-building interaction interfaces by shedding light on the choice of subjective thermal metrics to effectively and accurately collect information on occupants' thermal experience.

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“…For the group without fans, studies were selected from buildings in which none of the occupants used fans. The thermal comfort of occupants in AC buildings without fans was not included in the analysis because people in AC buildings are less adaptive to warm environments [25] and the comparison would not be influenced by adaptation.…”

Section: Evaluation Of Fan Effects 231 Thermal Comfortmentioning

confidence: 99%

Review of fan-use rates in field studies and their effects on thermal comfort, energy conservation, and human productivity

Chen

Wang

et al. 2019

Energy and Buildings

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This paper is a literature review of field studies on fan-use rates and their effects on thermal comfort, energy conservation, and human productivity. In the assessed literature, fans are more popular in Asia, and more used in mixedmode (MM) and naturally ventilated (NV) buildings than in air-conditioned (AC) buildings. On the basis of collected fan-use models, probit regression models of fan-use rates and ambient environments were obtained and indicate that the essential trigger of fan-use is a warm environment rather than building types. This result helps us to understand the control behaviors and comfort requirements of occupants. Also, fans could provide benefits in three aspects: widening neutral temperatures, saving energy, and improving occupants' productivity. First, using fans in buildings elevates the neutral temperature and the upper limit of neutral zone (0.5 thermal sensation scale) averages by about 3 K in ranges from 25.7℃ to 28.7℃ and 27.5℃ to 30.7℃, respectively. Second, fan-use reduces AC-use rates in MM buildings in summer. The regression models based on the collected AC-use rate models illustrate that, on average, AC-use is expected to be reduced by about 15% in summer when fans are used. Third, providing occupants access to fans could improve occupants' productivity. Based on the limited data available, a 3-K temperature extension is achieved by fans ensuring productivity not decreasing. This review could shed some light on the extension of the neutral temperature range, predictions of MM buildings' energy consumptions, and methods to enhance productivity. Additionally, this review suggests some valuable directions for future research on fans.

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Field study on adaptive comfort in air conditioned dormitories of university with hot-humid climate in summer

Cited by 62 publications

References 34 publications

Field study on adaptive thermal comfort in typical air conditioned classrooms

Field study on adaptive thermal comfort in typical air conditioned classrooms

Dimension analysis of subjective thermal comfort metrics based on ASHRAE Global Thermal Comfort Database using machine learning

Review of fan-use rates in field studies and their effects on thermal comfort, energy conservation, and human productivity

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