Editorial

Abstract

The Fanger's predicted mean vote (PMV) model is used to evaluate thermal comfort. However, when PMV is compared to people's real thermal sensations, collected in field studies, some discrepancies are verified. One of the components for the calculation of PMV is clothing surface temperature (tcl), which can be a factor that contributes towards these discrepancies. The aim of this study was to propose alternative methods for predicted mean vote, seeking to reduce these discrepancies. The mathematical Newton's method was applied to obtaining tcl values. The PMV1 was determined by replacing the tcl values in the traditional equation of PMV as described by ISO 7730 (2005). The second model of thermal prediction, named as PMV2, was obtained by a multiple linear regression considering the thermal sensation votes, the metabolic rate and the six heat exchange mechanisms. Two groups (welders and army officers) were used to verify the accuracy of the methods used in this research. The results show that both methods were able to describe the thermal sensation votes. For the welder group, both PMV1 and PMV2 overestimated the results: when people voted TSV = 0, PMV1 = 0.64 and PMV2 = 0.23. In the case of the army officers group, applying PMV1, when TSV = 0, PMV1 = 1.47. The application of the multiple regression increased the potential of PMV2 to obtain responses closer to those provided by the occupants of the thermal environment studied: when TSV = 0, PMV2 = 0.0068, demonstrating a greater effectiveness of this method.