Evaluating the overall comfort of undergraduates by using the punishment substitution method

Liu, Hong‐Min; Shi, Hao

doi:10.1177/1420326x211060758

Cited by 3 publications

(3 citation statements)

References 32 publications

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“…Abuja is in Nigeria's Northcentral region, and its area is approximately 1994 km 2 . It lies between latitude 9 o 03 0 and 9 o 07 0 N and longitude 7 o 26 0 and 7 o 39 0 E 36 and at an altitude of 840 m above sea level.…”

Section: Location Of the Case Study And Climatementioning

confidence: 99%

“…It is an environmental component that significantly impacts health and well-being. 1,2,3 The indoor climate warrants special attention because, in modern society, people spend a more substantial part of their life indoors. 1,4,5,6 The top indoor environmental quality element that generates the most concern amongst building occupants is thermal comfort.…”

Section: Introductionmentioning

confidence: 99%

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Examining thermal comfort levels and ASHRAE Standard-55 applicability: A case study of free-running classrooms in Abuja, Nigeria

Mustapha

Hassan

Khozaei

et al. 2023

Indoor and Built Environment

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Most thermal comfort research has traditionally focused on office and commercial buildings as opposed to educational facilities. Due to the high population density in schools and the undesirable impact an unsuitable thermal setting can have on performance, teaching and learning, providing thermal comfort in school facilities is crucial. Considering the above information, this study aims to collect empirical data from secondary schools in Abuja’s hot and dry climate to evaluate thermal comfort settings for students in naturally ventilated classrooms and the applicability of ASHRAE Standard-55 in the area. To achieve this, field investigations were conducted in free-running classrooms from four secondary schools. The study involved 1448 students drawn from 41 classrooms. Data collection involved measuring classroom thermal comfort variables and respondent survey, simultaneously performed while students were learning in the classroom. Findings revealed none of the studied classrooms’ thermal conditions met the ASHRAE Standard-55 comfort range. However, 44% of students felt comfortable. The study indicated a regression-neutral temperature of 28.8°C and a ‘comfort temperature’ range of 26 to 31°C. The findings of this research could form the foundation data for a local thermal comfort standard. However, the prior thermal experiences of respondents were not investigated in this study.

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Section: Location Of the Case Study And Climatementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Examining thermal comfort levels and ASHRAE Standard-55 applicability: A case study of free-running classrooms in Abuja, Nigeria

Mustapha

Hassan

Khozaei

et al. 2023

Indoor and Built Environment

View full text Add to dashboard Cite

show abstract

“…Owing to their thermal adaptability, people can achieve a comfortable state in a warm (cold) environment by changing their clothes. [46][47][48][49][50][51] Clothing insulation is the physical quantity of clothing impeding heat transferability. In 1970, Fanger 4 considered clothing insulation as an important parameter affecting thermal comfort in the PMV model.…”

Section: Clothing Insulationmentioning

confidence: 99%

Predicted mean vote model for thermal comfort evaluation of pregnant women considering the effects of metabolic rate

Fang

et al. 2022

Indoor and Built Environment

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In this study, on-site measurements of the summer air-conditioning environment in obstetric clinics of six hospitals in Guangzhou, China, and subjective questionnaire surveys of pregnant women regarding hot and humid environments were carried out. A total of 723 valid questionnaires were obtained. In order to determine the appropriate metabolic rate, three methods were used in this study to obtain three different metabolic rate values, including ASHRAE 55-2020 for healthy adults in a sedentary state 1.0 met, pregnant women after 5 months of pregnancy 1.2 met based on medical tests and the resting metabolic rate of pregnant women 0.66 met. Linear fitting was performed with three predicted mean votes (PMVs) and thermal sensation vote (TSV). The mean TSV (MTSV) and PMV models were established to estimate the actual thermal neutral value (25.3°C) and the predicted thermal neutral value (26.1°C) for pregnant women. The PMV model for pregnant women was linearly corrected, and the corrected PMV model coincided with the actual thermal sensory MTSV model. The corrected PMV estimate was 0.60 PMV + 0.17, and the corrected thermal neutral estimate was 25.3°C. Finally, a simplified model of the relationship between the PMV for pregnant women and the relative humidity, mean radiant temperature, wind speed and air temperature was obtained.

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Optimization of indoor quality and thermal comfort for university classrooms using data-based machine learning

Jiang,

Liu,

Yang

2024

E3S Web Conf.

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Improving indoor environment quality on university classrooms is a hot research topic. The on-site experiment was conducted on indoor environmental quality regarding temperature, humidity, air pollutants, light and acoustics during different seasonal conditions. The result shows that nearly 25% of indoor particulate matters exceeded the GB18883 standard when the outdoor environmental pollution was severe under natural ventilation conditions in autumn. More than 20% of students experienced symptoms of drowsiness, dizziness, chest tightness, poor breathing, as well as depression and irritability. From the analysis of occupant demand, indoor air pollution and thermal comfort are the most anticipated areas for students to improve their learning environment. This paper proposes an optimal IEQ prediction model integrated with students’ satisfaction and indoor environmental features using machine-learning classification algorithms. The back-propagation neural network shows the high prediction accuracy among different algorithms. The traditional PMV-PPD model shows an accuracy rate of only 28% for thermal sensation prediction, while the highest prediction accuracy obtained through machine learning algorithms is about 75%. Moreover, the influence of individual’s thermal adaptation ability, including gender, long-term thermal experience, and psychological factors, and environmental factors was analyzed in this study.

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Evaluating the overall comfort of undergraduates by using the punishment substitution method

Cited by 3 publications

References 32 publications

Examining thermal comfort levels and ASHRAE Standard-55 applicability: A case study of free-running classrooms in Abuja, Nigeria

Examining thermal comfort levels and ASHRAE Standard-55 applicability: A case study of free-running classrooms in Abuja, Nigeria

Predicted mean vote model for thermal comfort evaluation of pregnant women considering the effects of metabolic rate

Optimization of indoor quality and thermal comfort for university classrooms using data-based machine learning

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