Abstract
This paper studies the sensitivity of the Predicted Mean Vote (PMV) thermal comfort model relative to its environmental and personal parameters of a group of people in a space. PMV model equations, adapted in ASHRAE Standard 55eThermal Environmental Conditions for Human Occupancy, are used in this investigation to conduct parametric study by generating and analyzing multi-dimensional comfort zone plots. It is found that personal parameters such as metabolic rate and clothing have the highest impact. However, as these parameters are difficult to estimate or measure, they are usually assumed to be default values (rest conditions and light clothing). In this work, we show the application of the human-in-theloop sensor data of wearable devices to provide a continuous feedback for the averaged metabolism value of building occupants to be used in the PMV calculation. Moreover, we motivate the use of these sensor data to develop a new personalized comfort model.
