Personal factors in thermal comfort assessment: clothing properties and metabolic heat production

Havenith, George; Holmér, Ingvar; Parsons, Ken

doi:10.1016/s0378-7788(02)00008-7

Cited by 275 publications

(213 citation statements)

References 6 publications

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“…Differences between the two approaches might be justified by the inaccuracy of the estimation of personal parameters such metabolic rate and thermal insulation. Part of the inaccuracy of the methods for metabolic rate determination, is produced by the limited task description (Havenith et al, 2002). However, several extensive field studies summarized by Dear and Brager (1998) showed that in buildings running with centrally controlled HVAC systems, the PMV model approximates the observed thermal comfort of occupants quite closely.…”

Section: Discussionmentioning

confidence: 99%

Analytical and subjective interpretation of thermal comfort in hospitals: A case study in two sterilization services

Carvalhais

Baptista

Silva

2016

Journal of Toxicology and Environmental Health, Part A

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Section: Discussionmentioning

confidence: 99%

Analytical and subjective interpretation of thermal comfort in hospitals: A case study in two sterilization services

Carvalhais

Baptista

Silva

2016

Journal of Toxicology and Environmental Health, Part A

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“…Havenith et al [6] discussed the representation and measurement of clothing parameters and metabolic rate in the PMV context. It was observed that the impacts of body motion and air movement are relevant and thus they must be taken into account for the comfort prediction related to the clothing insulation [6]. Gauthier and Shipworth showed that in the PMV-PPD calculations the most influential parameter are the metabolic activity and the clothing insulation [7].…”

Section: Introductionmentioning

confidence: 99%

Influence of Three Dynamic Predictive Clothing Insulation Models on Building Energy Use, HVAC Sizing and Thermal Comfort

Schiavon

2014

Energies

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In building energy simulation, indoor thermal comfort condition, energy use and equipment size are typically calculated based on the assumption that the clothing insulation is equal to a constant value of 0.5 clo during the cooling season and 1.0 clo during the heating season. The assumption is not reflected in practice and thus it may lead to errors. In reality, occupants frequently adjust their clothing depending on the thermal conditions, as opposed to the assumption of constant clothing values above, indicating that the clothing insulation variation should be captured in building simulation software to obtain more reliable and accurate results. In this study, the impact of three newly developed dynamic clothing insulation models on the building simulation is quantitatively assessed using the detailed whole-building energy simulation program, EnergyPlus version 6.0. The results showed that when the heating ventilation and air conditioning system (HVAC) is controlled based on indoor temperature the dynamic clothing models do not affect indoor operative temperatures, energy consumption and equipment sizing. When the HVAC is controlled based on the PMV model the use of a fixed clothing insulation during the cooling (0.5 clo) and heating (1.0 clo) season leads to the incorrect estimation of the indoor operative temperatures, energy consumption and equipment sizing. The dynamic clothing models significantly (p < 0.0001) improve the ability of energy simulation tools to assess thermal comfort. The authors recommend that the dynamic clothing models should be implemented in dynamic building energy simulation software such as EnergyPlus. OPEN ACCESSEnergies 2014, 7 1918

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“…A comfortable indoor environment for all the occupants in a building is difficult to reach because of individual differences between those occupants. Based on the literature, it is concluded that individual differences, including: age (van Oeffelen, 2007), gender (Choi, Aziz, & Loftness, 2010;Karjalainen, 2007), percentage body fat (Zhang, Huizenga, Arens, & Yu, 2001), metabolism (Havenith, Holmér, & Parson, 2002) and clothing resistance (De Carli, Olesen, Zarrella, & Zecchin, 2007), are of importance for the individual experienced thermal comfort. However, nowadays the Fanger comfort model (Fanger, 1970) is still mainly used to determine the (thermal) comfort inside office buildings.…”

Section: Introductionmentioning

confidence: 99%

Occupants’ behavioural impact on energy consumption: ‘human-in-the-loop’ comfort process control

Zeiler

Vissers

Maaijen

et al. 2013

Architectural Engineering and Design Management

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Optimizing comfort for occupants and its related energy use is becoming more important. Presently, however, heating ventilation air-conditioning (HVAC) system installations often do not work in practice effectively and efficiently as the behaviour of the occupants is not included. The results are comfort complaints as well as unnecessary high-energy consumption. As the end-user influence becomes even more important within the energy process of sustainable buildings, it is necessary to integrate the occupants in the buildings' performance control loop. Laboratory experiments were performed to look for a correlation between infrared (IR) sensor temperature registrations and individual perceived thermal comfort in an individually conditioned workplace. It proved that it is in principle possible to use the third finger skin temperature as a control parameter for perceived thermal comfort. In another experiment in a real in-use office building, a wireless sensor network was applied to describe user behaviour on room and floor level. The results showed that it is possible to capture individual user behaviour and to use this to further optimize comfort in relation to energy consumption. Based on our experiments, we could determine the influence of occupants' behaviour on energy use and determine possible energy reduction by implementing the human-in-the-loop process control strategy.

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Personal factors in thermal comfort assessment: clothing properties and metabolic heat production

Cited by 275 publications

References 6 publications

Analytical and subjective interpretation of thermal comfort in hospitals: A case study in two sterilization services

Analytical and subjective interpretation of thermal comfort in hospitals: A case study in two sterilization services

Influence of Three Dynamic Predictive Clothing Insulation Models on Building Energy Use, HVAC Sizing and Thermal Comfort

Occupants’ behavioural impact on energy consumption: ‘human-in-the-loop’ comfort process control

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