Investigating the gender differences in indoor thermal comfort perception for summer and winter seasons and comparison of comfort temperature prediction methods

Asif, Ayesha; Zeeshan, Muhammad; Khan, Shoaib Raza; Sohail, Nabia Farrukh

doi:10.1016/j.jtherbio.2022.103357

Cited by 24 publications

(8 citation statements)

References 53 publications

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“…Thus, there are significant thermal sensation differences between males and females. This result is consistent with a previous study on student dormitory buildings in Pakistan [42]. Interestingly, this study and the previous study [42] yielded similar trends regarding the significant differences in thermal sensations between males and females, despite the differences in subjects and climates.…”

Section: Thermal Sensation Votesupporting

confidence: 93%

Development of Adaptive Model and Occupant Behavior Model in Four Office Buildings in Nagasaki, Japan

Genjo,

Nakanishi,

Oki

et al. 2023

Energies

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A field survey of indoor environmental measurements and questionnaires on thermal sensation, overall comfort, and behaviors was conducted in four office buildings in Japan by visiting each office every month over a duration of more than a year during the coronavirus disease 2019 (COVID-19) pandemic. The indoor environment was measured concurrently. We obtained 1047 votes from office workers in their 20s to 60s. The regression and Griffiths’ methods were used to calculate the indoor comfort temperature. A logistic regression analysis was used to develop the occupant behavior model. Over 70% of the occupants found the indoor environment comfortable at a mean comfort temperature of 23.2 to 25.9 °C. Gender differences were observed in thermal sensation and overall comfort, but a gender difference was observed only in the cooling mode for the indoor comfort temperature. An adaptive model was developed for the office buildings in Nagasaki city to predict the indoor comfort temperature from the outdoor air temperature. The proportions of heating, cooling, and fan usage can be predicted from the outdoor air temperature using a logistic regression analysis. The adaptive model and occupant behavior model are useful for the indoor temperature control of the existing buildings and thermal simulation of the new building design.

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Section: Thermal Sensation Votesupporting

confidence: 93%

Development of Adaptive Model and Occupant Behavior Model in Four Office Buildings in Nagasaki, Japan

Genjo,

Nakanishi,

Oki

et al. 2023

Energies

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“…The research subjects involved were male and female respondents. In thermal comfort research, it is possible that there are differences in the results of thermal sensation between men and women, although there are studies that say there are not many differences between men and women [32]. Research subjects were not selected using sampling.…”

Section: Methodsmentioning

confidence: 99%

Thermal Comfort Prediction Accuracy with Machine Learning between Regression Analysis and Naïve Bayes Classifier

et al. 2022

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Various data analysis methods can make thermal comfort prediction models. One method that is often used is multiple linear regression statistical analysis. Regression analysis needs to be checked for accuracy with other analytical methods. This study compares the making of a thermal comfort prediction model with regression analysis and naïve Bayes analysis. The research method used quantitative methods for data collection regarding thermal comfort. The thermal comfort variable, consisting of eight independent variables and one dependent variable, was measured at Wonosobo High School, Indonesia. The analysis to make the prediction model was carried out with two different analyses: multiple linear regression analysis and naïve Bayes analysis. The results show that naïve Bayes is more accurate than multiple linear regression analysis.

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“…gender, age, familiarity to a space) also play important roles in comfort perception. 36,37 For example, Asif 38 found that comfort indoor temperatures of male and female present significant difference. Ageing of building and interior might also contribute to the indoor comfort perception.…”

Section: Occupancy Comfortmentioning

confidence: 99%

Solving the comfort-retrofit conundrum through post-occupancy evaluation and multi-objective optimisation

Wang

et al. 2023

Building Services Engineering Research and Technology

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Developing appropriate building retrofit strategies is a challenging task. This case study presents a multi-criteria decision-supporting method that suggests optimal solutions and alternative design references with a range of diversity at the early exploration stage in building retrofit. This method employs a practical two-step method to identify critical comfort and energy issues and generate optimised design options with multi-objective optimisation based on a genetic algorithm. The first step is based on a post-occupancy evaluation, which cross-refers benchmarking and correlation and integrates them with non-linear satisfaction theory to extract critical comfort factors. The second step parameterises previous outputs as objectives to conduct building simulation practice. The case study is a typical post-war highly glazed open-plan office in London. The post-occupancy evaluation result identifies direct sunlight glare, indoor temperature, and noise from other occupants as critical comfort factors. The simulation and optimisation extract the optimal retrofit strategies by analysing 480 generated Pareto fronts. The proposed method provides retrofit solutions with a criteria-based filtering method and considers the trade-off between the energy and comfort objectives. The method can be transformed into a design-supporting tool to identify the key comfort factors for built environment optimisation and create sustainability in building retrofit. Practical application: This study suggested that statistical analysis could be integrated with parametric design tools and multi-objective optimisation. It directly links users’ subjective opinions to the final design solutions, suggesting a new method for data-driven generative design. As a quantitative process, the proposed framework could be automated with a program, reducing the human effort in the optimisation process and reducing the reliance on human experience in the design question defining and analysis process. It might also avoid human mistakes, e.g. overlooking some critical factors. During the multi-objective optimisation process, large numbers of design options are generated, and many of them are optimised at the Pareto front. Exploring these options could be a less human effort-intensive process than designing completely new options, especially in the early design exploration phase. Overall, this might be a potential direction for future study in generative design, which greatly reduce the technical obstacle of sustainable design for high building performance.

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Investigating the gender differences in indoor thermal comfort perception for summer and winter seasons and comparison of comfort temperature prediction methods

Cited by 24 publications

References 53 publications

Development of Adaptive Model and Occupant Behavior Model in Four Office Buildings in Nagasaki, Japan

Development of Adaptive Model and Occupant Behavior Model in Four Office Buildings in Nagasaki, Japan

Thermal Comfort Prediction Accuracy with Machine Learning between Regression Analysis and Naïve Bayes Classifier

Solving the comfort-retrofit conundrum through post-occupancy evaluation and multi-objective optimisation

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