Collective comfort optimization in multi-occupancy environments by leveraging personal comfort models and thermal distribution patterns

Buildings with shared spaces present a unique challenge for maintaining thermal comfort due to their dynamic occupancy patterns and the potential for wide variation in occupant thermal preferences and tolerances. Conventional approaches aim to achieve relatively uniform temperature conditions throughout a space, which implies that the thermal environment will likely be suboptimal for many of the individual occupants. Recent research efforts have integrated personal comfort models with heating, ventilation and air conditioning (HVAC) controls and have shown promising improvements by taking a highly individualistic approach to evaluating thermal comfort and adjusting HVAC operations accordingly. In this work, we aim to further advance occupantcentric controls by evaluating the benefits that could be gained by explicitly influencing and leveraging the development of non-uniform thermal conditions within a space. In particular, we consider the context of a multioccupancy open office space shared by six occupants, where the thermal distribution patterns can be influenced by controlling the direction and flow rate of conditioned air being supplied through a central diffuser. Computational fluid dynamics was used to model and simulate thermal distribution patterns. Six probabilistic thermal comfort profiles were used to quantify the likelihood of each occupant being comfortable under the various control settings and location assignments. We analyzed three control strategies with incremental complexity, and collective comfort probability was shown to improve by 11%, 22%, and 30%, respectively. Our results also showed the potential energy-saving pathway by altering supply airflow direction instead of changing supply airflow rate to adjust thermal conditions in shared environments.