Vision Based Dynamic Thermal Comfort Control Using Fuzzy Logic and Deep Learning

A wide range of techniques exist to help control the thermal comfort of an occupant in indoor environments. A novel technique is presented here to adaptively estimate the occupant's metabolic rate. This is performed by utilising occupant's actions using computer vision system to identify the activity of an occupant. Recognized actions are then translated into metabolic rates. The widely used Predicted Mean Vote (PMV) thermal comfort index is computed using the adaptivey estimated metabolic rate value. The PMV is then used as an input to a fuzzy control system. The performance of the proposed system is evaluated using simulations of various activities. The integration of PMV thermal comfort index and action recognition system gives the opportunity to adaptively control occupant's thermal comfort without the need to attach a sensor on an occupant all the time. The obtained results are compared with the results for the case of using one or two fixed metabolic rates. The included results appear to show improved performance, even in the presence of errors in the action recognition system.