Automatic optimization of individual thermal comfort in indoor spaces shared by multiple occupants is difficult, because it requires understanding of the individual thermal comfort preferences, modeling of the room thermodynamics, and fast online optimization to account for movements of the occupants. We explore an approach to optimizing individual thermal comfort subject to the seating arrangement of a group of individuals through temperature set-point optimization of Heating, Ventilation, and Air Conditioning (HVAC) equipment.
In this paper, we learn both the individual thermal comfort preferences using a weakly supervised approach and the room thermodynamics via static approximations. Finally, we use optimization to determine the HVAC set points that maximize individual thermal comfort subject to the current seating arrangement. The proposed method is tested on a real data set obtained from workers in an open office.
The results show that, on average, the temperature in the room at each user's location can be regulated on average to within 0.85C of the user's desired temperature, with a standard deviation of 0.12C.