The use of Monte Carlo method to assess the uncertainty of thermal comfort indices PMV and PPD: Benefits of using a measuring set with an operative temperature probe

Abstract

The Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD) are the most used indices for the assessment of thermal conditions in indoor environments. However, many times, the uncertainties associated with the calculation of both indices are not reported, may be because direct methods are not easily applicable to calculate it. The present study applies Monte Carlo method to assess the uncertainties on the calculation of PMV and PPD, as a function of values and the uncertainties of four environmental (air temperature, mean radiant temperature, air velocity, and partial vapour pressure) and two individual related input parameters (metabolic rate and clothing insulation), used in Fanger's model, to calculate it. The metrological quality of the measuring probes was assumed through the scenarios established by ISO 7726 (1998) (required and desirable conditions). The use of uncertainties values for metabolic rate, clothing insulation and operative temperature were also considered. The main findings of this research are: (i) condition defined as required is not suitable for implementation of the classification scheme of thermal environments proposed by ISO 7730 (2005); (ii) in desirable condition, it is unrealistic obtaining an uncertainty of 0.2 °C for mean radiant temperature, if a 0.2 °C uncertainty temperature probe is used; (iii) the use of an operative temperature probe is a good strategy to decrease the overall level of uncertainty in the indices.