Personalizing Individual Comfort in the Group Setting


  • Emil Laftchiev Mitsubishi Electric Research Labs
  • Diego Romeres Mitsubishi Electric Research Labs
  • Daniel Nikovski Mitsubishi Electric Research Labs


Thermal Comfort Optimization, Weakly Supervised Modeling, Predictive Modeling, Neural Networks, HVAC Optimization


Maintaining individual thermal comfort in indoor spaces shared by multiple occupants is difficult because it requires both intuition about the thermal properties of the room, as well as an understanding of the thermal comfort preferences of each individual. We explore an approach to optimizing individual thermal comfort within a group through temperature set-point optimization of HVAC equipment. We propose a weakly-supervised algorithm to learn the individual thermal comfort preferences and an autoencoding framework to learn static approximations of room thermodynamics. We further propose two approaches to learn a control law that sets the HVAC set-points subject to the preferred user temperatures. 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 to within 0.5C of the user's desired temperature.




How to Cite

Laftchiev, E., Romeres, D., & Nikovski, D. (2021). Personalizing Individual Comfort in the Group Setting. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15339-15346. Retrieved from



IAAI Technical Track on Emerging Applications of AI