The approximation between thermal sensation votes (TSV) and predicted mean vote (PMV): A comparative analysis

Broday, Evandro Eduardo; Moreto, Jéferson Aparecido; Xavier, Antônio Augusto de Paula; Oliveira, Reginaldo de

doi:10.1016/j.ergon.2018.09.007

Cited by 41 publications

(24 citation statements)

References 26 publications

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“…The index is used both for evaluation in existing buildings and in building simulations for the prediction of thermal comfort levels [34]. The lack of clear information in technical Standards and wrong interpretations of the standards [29,[35][36][37] result in increased inaccuracy in its calculation. This also applies to microclimatic dataloggers provided with built-in software.…”

Section: Aim Of the Papermentioning

confidence: 99%

Fifty Years of PMV Model: Reliability, Implementation and Design of Software for Its Calculation

Alfano

Olesen²,

Palella

et al. 2019

Atmosphere

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In most countries, PMV is the reference index for the assessment of thermal comfort conditions in mechanically conditioned environments. It is also the basis to settle input values of the operative temperature for heating and cooling load calculations, sizing of equipment, and energy calculations according to EN 16798-1 and 16798-2 Standards. Over the years, great effort has been spent to study the reliability of PMV, whereas few investigations were addressed to its calculation. To study this issue, the most significant apps devoted to its calculation have been compared with a reference software compliant with EN ISO 7730 and the well-known ASHRAE Thermal Comfort Tool. It has been revealed that only few apps consider all six variables responsible for the thermal comfort. Relative air velocity is not considered by ASHRAE Thermal Comfort Tool and, finally, the correction of basic insulation values due to body movements introduced by EN ISO 7730 and EN ISO 9920 Standards has only been considered in one case. This implies that most software and apps for the calculation of PMV index should be used with special care, especially by unexperienced users. This applies to both research and application fields.Atmosphere 2020, 11, 49 2 of 14 buildings addressing IEQ, whereas EN 16798-2 explains how to use EN 16798-1 by specifying additional information as: (i) input parameters for building system design and energy performance calculations; (ii) methods for long term evaluation of the indoor environment; (iii) criteria for measurements which can be used if required to measure compliance by inspection; (iv) parameters to be used by monitoring and displaying the indoor environment in existing buildings. With reference to thermal comfort, EN 16798-1 suggests specific design ranges of operative temperature consistent with the desired level of environmental quality (see Table 1). Table 1. Temperature ranges for hourly calculation of cooling and heating energy for some indoor environment (Category II) according to EN 16798-1 and EN 16798-2 Standards [11,12]. Resultant insulation values I cl,r to be used [6] are 0.5 clo (cooling) and 1.0 clo (heating). Type of Building SpaceOperative Temperature Range for Heating ( • C) Operative Temperature Range for Cooling ( • C) Offices and spaces with similar activity (single offices, open plan offices, conference rooms, auditorium, cafeteria, restaurants, and classrooms). Sedentary activity: M = 1.2 met 20.0-24.0 23.0-26.0 Department store Standing-walking activity: M = 1.6 met 16.0-22.0 21.0-25.0

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Section: Aim Of the Papermentioning

confidence: 99%

Fifty Years of PMV Model: Reliability, Implementation and Design of Software for Its Calculation

Alfano

Olesen²,

Palella

et al. 2019

Atmosphere

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“…Calculations of thermal sensations are commonly done by Fanger's energy balance models. 13 However, more recent experimental studies have shown that observed and calculated values might differ considerably [14][15][16][17][18] leading to the development of theories explaining the differences according to factors such as "short-term thermal adaptation" and "thermal expectation." 19 Additionally, thermal sensation of females and males in the same thermal environment, activity, and clothing insulation has been reported as being consistently different.…”

Section: Theory and Aimmentioning

confidence: 99%

“…Calculations of thermal sensations are commonly done by Fanger's energy balance models . However, more recent experimental studies have shown that observed and calculated values might differ considerably leading to the development of theories explaining the differences according to factors such as “short‐term thermal adaptation” and “thermal expectation.”…”

Section: Introductionmentioning

confidence: 99%

Season influence on rapid thermal sensation assessment by young adults

Coelho

Silva

2019

Engineering Reports

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Thermal comfort is one of the most important ergonomic aspects of building occupancy. In this research, laboratory experiments are performed in a climatic chamber and described in detail. Experiments are carried out under two scenarios: with two different college students cohorts and with five different but comparable experimental conditions in each cohort. Three hundred twenty‐two individual assessments under specific controlled thermal environment conditions are collected. The actual thermal sensation assessments obtained in the experiments are compared to the results obtained by a predicted mean vote (PMV) model. The correlation analysis shows that statistically significant differences are meaningful between the spring‐summer and the autumn‐winter experiments but not between genders. This paper discusses the plausible factors contributing to the different correlations experienced in the autumn‐winter and spring‐summer experiments. A correction factor between PMV according to Fanger's comfort equation and the actual thermal sensation values reported by the participants is also sought with a focus on the seasonal effects. The predicted results are in good agreement with the experimental results. This allows for further considerations about the influence of the season on the initial thermal sensations experienced by young adults.

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“…However, the deficiency of PMV has been evidenced by compared with the subjective thermal sensation votes [2,12]. The PMV could overestimate or underestimate the thermal sensation [13][14][15][16]. Humphreys and Nicol [17] confirmed that the biases in PMV exceeded 0.25 scale frequently and reached as much as one scale through meta-analysis.…”

Section: Introductionmentioning

confidence: 99%

“…The high sensitivity of PMV to the metabolic rate has been demonstrated that changing the metabolic rate by ±10% resulted in a variation in PMV from -0.16 scale to 0.14 scale [18]. It has been experimentally confirmed that an accurate input of the metabolic rate can efficiently improve the PMV for the thermal sensation prediction [12,13,19,21].…”

Section: Introductionmentioning

confidence: 99%

Improving predicted mean vote with inversely determined metabolic rate

Zhang

Cheng

Oladokun

et al. 2020

Sustainable Cities and Society

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The approximation between thermal sensation votes (TSV) and predicted mean vote (PMV): A comparative analysis

Cited by 41 publications

References 26 publications

Fifty Years of PMV Model: Reliability, Implementation and Design of Software for Its Calculation

Fifty Years of PMV Model: Reliability, Implementation and Design of Software for Its Calculation

Season influence on rapid thermal sensation assessment by young adults

Improving predicted mean vote with inversely determined metabolic rate

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