Uma análise das condições de conforto térmico no Brasil por meio do banco de dados ASHRAE Global Thermal Comfort Database II

• Ano de defesa: 2022
• Autor(a) principal: Niza, Iasmin Lourenço
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Ponta Grossa; Brasil; Programa de Pós-Graduação em Engenharia de Produção; UTFPR
• Palavras-chave em Português: Ambiente de trabalho; Conforto humano; Temperatura - Efeito fisiológico; Banco de dados; Work environment; Human comfort; Temperature - Physiological effect; Data bases; CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO; Engenharia/Tecnologia/Gestão
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/27643

The Predicted Mean Vote (PMV) developed by Fanger in the 1970s is the most widely used model to estimate the thermal sensation in a group of people. The PMV has some discrepancies regarding the thermal reality of the environments where it is applied, so several researchers have developed alternative models to reduce these discrepancies. This research aimed to determine which of the alternative models to the PMV best approximates the real thermal sensation of people in four Brazilian cities: Brasília, Recife, Maceió and Florianópolis. Using ASHRAE's Global Thermal Comfort Database II, the environmental and personal thermal comfort variables were applied to calculate the responses of each model and thus compare them to the thermal sensation responses. The PMV model was not suitable for any Brazilian city. For Recife and Brasilia, Orosa and Oliveira's (2011) model, the PMVoo showed a mean difference with a closer 0.102801 to the thermal sensation votes; for Maceió, Broday et al. (2019) model, the PMV2 with 0.161065 and for Florianópolis, Zhang and Lin's (2020) model, the ePMV with 0.031096. In the discriminant analysis, the model got the classification of individuals into groups right with 96.1% for Brasilia and Recife; 99.8% for Maceió and 99.1% for Florianópolis. In the cluster analysis the similarities between subjects were observed in relation to their grouping, thus, the PMVnew2 model was the variable with the best discrimination between the clusters for Florianopolis and PMVp,sv for Brasilia, Recife, and Maceió. The alternative models performed better than the traditional model.