Improving predicted mean vote with inversely determined metabolic rate

Zhang, Sheng; Cheng, Yong; Oladokun, Majeed Olaide; Wu, Yuxin; Lin, Zhang

doi:10.1016/j.scs.2019.101870

Cited by 53 publications

(13 citation statements)

References 56 publications

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“…It noted that the field data of the three types of buildings selected for the validations of the proposed arPMV are from the three widely cited papers on adaptive thermal comfort models 32,38,54 . More data from existing studies can be extracted to further validate the proposed arPMV, for example, the field data from Zhang et al, 33 Yu et al, 47 and Liu et al 60 Reassuring validations of the proposed arPMV are not presented.…”

Section: Validation Through Study Of Mixed‐mode Building Casesmentioning

confidence: 99%

“…Efforts have been made to extend the capacities of the rational approach for explaining the thermal adaptations. One method is to take into account the effects of the thermal adaptations on the inputs of the PMV 30,33 . Schweiker and Wagner 34 set up equations for each of the three categories of thermal adaptations individually to determine the inputs of the PMV (ie, the clothing level and metabolic rate).…”

Section: Introductionmentioning

confidence: 99%

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Adaptive‐rational thermal comfort model: Adaptive predicted mean vote with variable adaptive coefficient

Zhang

2020

Indoor Air

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Thermal adaptations, as feedbacks of occupants to physical stimuli, extend thermal comfort zone thereby reducing building energy consumption effectively. The rational approach models thermal comfort from the perspective of the body's heat balance, but is limited in explaining the thermal adaptations. The adaptive approach of modeling thermal comfort can fully account for the thermal adaptations, but ignores the body's heat balance. To improve thermal comfort prediction, this study proposes an adaptive‐rational thermal comfort model, that is, an adaptive predicted mean vote with a variable adaptive coefficient (termed as arPMV). By linearly linking the negative feedback effects of the thermal adaptations to the ambient temperature according to the adaptive approach, the variable adaptive coefficient is linearly related to the reciprocal of the ambient temperature with two constants. The variable adaptive coefficient is determined by explicitly quantifying the two constants as the functions of the predicted mean vote, thermal sensation vote, and ambient temperature. The proposed arPMV is validated for naturally ventilated, air‐conditioned, and mixed‐mode buildings, with the mean absolute error and the robustness of the thermal sensation prediction reduced by 24.8%‐83.5% and improved by 49.7%‐83.4%, respectively.

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Section: Validation Through Study Of Mixed‐mode Building Casesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive‐rational thermal comfort model: Adaptive predicted mean vote with variable adaptive coefficient

Zhang

2020

Indoor Air

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“…Many factors would affect the thermal history of occupants, including air temperature, metabolic rate (Zhang et al, 2020), mean radiation temperature, humidity (Cai et al, 2020), wind speed/draught (Wu et al, 2021b), and clothing insulation (Liu et al, 2018). For the comprehensive indoor environments, the standard effective temperature (SET) (DA., 1980), which was calculated from the Gagge two-node model (Gagge, 1986), was used to consider these factors with a standardized index.…”

Section: Introductionmentioning

confidence: 99%

Individual thermal comfort prediction using classification tree model based on physiological parameters and thermal history in winter

Hong

et al. 2021

Build. Simul.

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Individual thermal comfort models based on physiological parameters could improve the efficiency of the personal thermal comfort control system. However, the effect of thermal history has not been fully addressed in these models. In this study, climate chamber experiments were conducted in winter using 32 subjects who have different indoor and outdoor thermal histories. Two kinds of thermal conditions were investigated: the temperature dropping (24-16°C) and severe cold (12°C) conditions. A simplified method using historical air temperature to quantify the thermal history was proposed and used to predict thermal comfort and thermal demand from physical or physiological parameters. Results show the accuracies of individual thermal sensation prediction was low to about 30% by using the PMV index in cold environments of this study. Base on the sensitivity and reliability of physiological responses, five local skin temperatures (at hand, calf, head, arm and thigh) and the heart rate are optimal input parameters for the individual thermal comfort model. With the proposed historical air temperature as an additional input, the general accuracies using classification tree model C5.0 were increased up to 15.5% for thermal comfort prediction and up to 29.8% for thermal demand prediction. Thus, when predicting thermal demands in winter, the factor of thermal history should be considered.

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“…Compared with the adaptive approach‐based thermal comfort models that predict the thermal sensation/comfortable temperature from the ambient temperature only, the PMV accounts for more parameters relating to the heat balance of the body for thermal comfort ( eg , air velocity and metabolic rate) 19,20 . However, the PMV is derived from the chamber experiments which are unable to reflect thermal adaptations in real life 21‐24 . To improve the capability of PMV in explaining thermal adaptations, Fanger and Toftum 25 proposed an expectancy factor for the PMV to account for the psychological adaptation partially, and Yao et al 26 proposed an adaptive coefficient for the PMV to account for the psychological and behavioral adaptations.…”

Section: Introductionmentioning

confidence: 99%

Extended predicted mean vote of thermal adaptations reinforced around thermal neutrality

Zhang

Lin

2021

Indoor Air

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For acceptable indoor environments, buildings account for a large portion of primary energy. [1][2][3] While technological innovations have been the center for improving energy efficiency in buildings, 4-7 the role of occupants' thermal adaptations (physiological, psychological, and behavioral adaptations) is receiving more and more attention in reducing the energy use for thermal comfort in buildings. [8][9][10][11] For example, Ming et al 12 found that the adaptive thermal comfort saved cooling energy in office buildings in Chongqing by 34% and Du et al 13 revealed that the adaptive thermal comfort reduced the cooling and heating energy by around 20-35% in residential buildings across the hot summer and cold winter zone of China. Ignoring thermal adaptations could cause excessive cooling and heating with thermal discomfort and energy wastage. 14 Thus, thermal comfort models used for building design and operation should take into account thermal adaptations for thermal comfort and energy efficiency of buildings. 15,16

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Improving predicted mean vote with inversely determined metabolic rate

Cited by 53 publications

References 56 publications

Adaptive‐rational thermal comfort model: Adaptive predicted mean vote with variable adaptive coefficient

Adaptive‐rational thermal comfort model: Adaptive predicted mean vote with variable adaptive coefficient

Individual thermal comfort prediction using classification tree model based on physiological parameters and thermal history in winter

Extended predicted mean vote of thermal adaptations reinforced around thermal neutrality

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