Abstract
This research concerns a first approach to adapt the thermal comfort bands of the Physiological Equivalent Temperature (PET), New Standard Effective Temperature (SET), and Predicted Mean Vote (PMV) indices to Santa Maria’s population, Rio Grande do Sul, Brazil, on the basis of the application of perception/sensation questionnaires to inhabitants while, at the same time, recording meteorological attribute data. Meteorological and thermal sensation data were collected from an automatic weather station installed on paved ground in the downtown area, which contained the following sensors: a scale gauge; a global radiation sensor; a temperature and humidity sensor; a speed and wind direction sensor; a gray globe thermometer. First of all, air temperature, gray globe temperature, relative air humidity, wind speed, wind gust, global solar radiation and precipitation were collected. People were interviewed using a questionnaire adapted from the model established by ISO 10551. The results demonstrated the efficiency of the linear regression model and the adequacy of the interpretive indexes, presenting results different from those analyzed by other authors in different climatic zones. These differences meet the analyzed literature and attest to the effectiveness of the calibration method of the PET, SET, and PMV indices for the Brazilian subtropical climate. After calibration, the PET index hit rate increased from 32.8% to 69.3%. The SET index, which had an initial hit rate of 34.6% before calibration, reached a hit-rate of 64.9%, while the PMV index increased from 35.9% to 58.7%.
Highlights
Models for the prediction of thermal comfort employ seven- or nine-point thermal sensitivity scales for the assessment of the average person’s perception of atmospheric weather conditions in open spaces
New cutoff points were established for such indexes, which were based on two multiple-linear regression models [41] and the use of an optimization code of the proportion of hits of the models through changes in the cutoff points
We conclude that the analyzed scores allowed the estimation of the thermal comfort classes for an individual at a given time as a function of the values of the observed climatic conditions, which validates the linear regression method for determining the adequacy of the interpretive ranges of each index
Summary
Models for the prediction of thermal comfort employ seven- or nine-point thermal sensitivity scales for the assessment of the average person’s perception of atmospheric weather conditions in open spaces. Studies and human comfort indexes are being developed to measure thermal comfort in open spaces under uncontrolled conditions [6,7,8,9]. Some such studies focus on modeling and assessment methods from the thermophysiological perspective, e.g., those by Gulyas et al [10] and Hoppe [1], Climate 2018, 6, 24; doi:10.3390/cli6020024 www.mdpi.com/journal/climate
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
