Investigating Occupancy-Driven Air-Conditioning Control Based on Thermal Comfort Level

Current air-conditioning systems often rely on maximum occupancy assumptions and fixed schedules to maintain a sufficient comfort level. Having knowledge regarding the occupancy situation may lead to significant energy savings in a building. Therefore, the paper proposes a method to investigate an occupancy-driven HVAC control system that is based on thermal comfort analysis. Computational fluid dynamics (CFD) was used to evaluate thermal comfort through modeling of the indoor air distribution and flow. Air velocity and temperature were simulated in several scenarios and the predicted mean vote (PMV) and the predicted percentage dissatisfied (PPD) were computed. The simulation results were verified through a survey asking for occupants' feelings, and the consequential thermal comfort profiles were identified, which were used for creating possible energy savings. Moreover, a predefined working schedule and the historical behavior of persons were used to develop a pattern for predicting personal occupancy situations. Finally, all variables were imported into an intelligence system to fulfill intelligent control of the air-conditioning system. The results show good potential to reduce energy consumption while meeting the comfort requirements of occupants.