It is a challenge for traditional building control systems to meet occupants’ needs in shared spaces due to the lack of understanding of individual occupant thermal preferences. This is a barrier to balancing energy efficiency and indoor environmental quality (IEQ). Advanced statistical learning methods offer new solutions towards more energy-efficient and user-centric control logics. In this work, a control logic is proposed to optimise the heating, ventilation and air conditioning (HVAC) operation based on thermal comfort archetype preferences, leveraging the ASHRAE Global Thermal Comfort Database II in conjunction with energy simulations. First, we apply the k-means clustering algorithm to categorize occupants into different archetypes regarding their common feedback on the thermal environment. Then, we fit a Bayesian logistic regression model to predict the thermal comfort preferences of different archetypes based on IEQ data. Finally, we identify two occupant-centric control logics to optimize HVAC operation to meet occupants’ requirements: (i) considering a unified response of thermal comfort in the space, and (ii) ensuring the dynamic optimal setpoint when conflicting occupant archetypes are present. Having compared this control logic with a common rule-based logic, our results demonstrate the potential of occupant-centric controls and the importance of multi-objective metrics in accounting for energy efficiency.