E3S Web of Conf.
Volume 396, 2023The 11th International Conference on Indoor Air Quality, Ventilation & Energy Conservation in Buildings (IAQVEC2023)
|Number of page(s)||6|
|Section||Indoor Environmental Quality (IEQ), Human Health, Comfort and Productivity|
|Published online||16 June 2023|
The use of cluster analysis to assess thermal comfort in university classrooms
IEQ Lab, Federal University of Technology – Paraná (UTFPR), Rua Doutor Washington Subtil Chueire, 330, Jardim Carvalho, Ponta Grossa, PR, 84017-220, Brazil
Nowadays, providing health, well-being, productivity and energy efficiency to users inside buildings is essential. Applying these aspects aligned with sustainability becomes necessary to reduce the use of heating, ventilation, and air-conditioning (HVAC) systems. These systems are currently used to provide better thermal conditions to the occupants, who spend around 80% of their time indoors. The actual thermal conditions can be affected by several factors, such as the climatic type of the region, orientation, size, building type, and energy levels, among others. To assess thermal conditions inside buildings, several thermal comfort models have been developed over the years. However, the Predicted Mean Vote (PMV) created by Fanger is still the most common model to assess thermal comfort indoors. In this context, this research aimed to analyze thermal comfort conditions in university classrooms in Southern Brazil. By collecting the environmental and personal variables of thermal comfort and the mean thermal sensation of students through measurements and questionnaires, a total of 519 responses were obtained during the Brazilian autumn. A statistical cluster analysis was performed to classify individuals according to their sensations. Differences between genders were verified and changing indoor temperatures lower in winter would therefore save HVAC energy without impacting occupant comfort.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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