Thermal Comfort Assessment in University Classrooms: A Discriminant Analysis for Categorizing Individuals According to Gender and Thermal Preferences

Abstract

The concern with the well-being of users in buildings has become increasingly essential, covering aspects related to health, energy efficiency, and productivity. The thermal environment evaluation in buildings has become more frequent due to the time people spend inside them. In this context, this study aimed to analyze thermal comfort in classrooms at a Brazilian University. During the autumn, 50 measurements were performed, resulting in 519 valid responses. The results of the linear regression analysis revealed that the thermal comfort range for females was 20.39–22.19 °C, while for males it was 19.47–22.56 °C. Through discriminant analysis, participants were classified based on their thermal sensation vote (TSV), predicted mean vote (PMV), and thermal preference votes (PREF), achieving a success rate of 76.1% for females and 81.6% for males in forming the groups, which demonstrates the effectiveness of discriminant functions in predicting thermal comfort for both groups. These results highlight the importance of considering gender differences in the search for thermal comfort conditions and providing guidelines that promote the well-being of occupants and the conscious use of energy. This implies adjusting the thermal conditions according to the specific needs of males and females in classrooms, always seeking to provide a suitable environment for activities, and considering energy efficiency and users’ productivity.

Energy Efficiency and Thermal Comfort Analysis in a Higher Education Building in Brazil

“…The findings of our research work have some similarities and, of course, differences compared to other studies investigating thermal comfort based on surveys in university buildings. We selected seven studies with university classroom surveys for comparison, including countries with distinct climates and student behaviours, such as Australia (Alghamdi et al [58]), China (Jing et al [59]; Zhang et al [7]), Italy (Nico et al [60]), Ecuador (Guevara et al [40]), Indonesia (Hamzah et al [61]), and Brazil (Niza et al [62]). One selected topic was related to comparisons between the PMV and PPD votes collected in classes with the votes predicted by models.…”