Uncertainty Assessment of Mean Radiant Temperature Estimation for Indoor Thermal Comfort Based on Clustering Analysis of Reduced-Input Surfaces

Abstract

Mean radiant temperature (MRT) is important for indoor thermal comfort determination. Several good ways to practically obtain accurate MRT include measuring all indoor surface temperatures for MRT calculation or using a black globe thermometer. Still, it can be hard to apply in practice because using such experimental measurements increases the efforts of data management times and acquisition costs. In this regard, there is a practical advantage in reducing the number of measured surfaces by grouping similar surfaces rather than measuring all indoor surface temperatures individually to obtain MRT. However, since even those similar surfaces are not the same, it can lead to erroneous MRT estimation, which needs to be investigated. This study analyzes the uncertainty of MRT estimates by categorizing the surfaces with similar temperature behaviors to examine the risk of such inaccuracy. In this study, the input data required for the MRT calculation are generated using a measurement data-based simulation model, and the uncertainty of the MRT is quantified using the Monte Carlo method. As a result of the study, it is observed that excluding surfaces with similar temperatures for MRT estimation does not significantly affect the uncertainty. When the appropriate number of input surfaces is satisfied, its MRT shows a difference of less than 1% compared to the results calculated with all surfaces.