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

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