  Developing personalised thermal comfort models to inform occupant-centric
controls (OCC) in buildings requires collecting large amounts of real-time
occupant preference data. This process can be highly intrusive and
labour-intensive for large-scale implementations, limiting the practicality of
real-world OCC implementations. To address this issue, this study proposes a
thermal preference-based HVAC control framework enhanced with Active Learning
(AL) to address the data challenges related to real-world implementations of
such OCC systems. The proposed AL approach proactively identifies the most
informative thermal conditions for human annotation and iteratively updates a
supervised thermal comfort model. The resulting model is subsequently used to
predict the occupants' thermal preferences under different thermal conditions,
which are integrated into the building's HVAC controls. The feasibility of our
proposed AL-enabled OCC was demonstrated in an EnergyPlus simulation of a
real-world testbed supplemented with the thermal preference data of 58 study
occupants. The preliminary results indicated a significant reduction in overall
labelling effort (i.e., 31.0%) between our AL-enabled OCC and conventional OCC
while still achieving a slight increase in energy savings (i.e., 1.3%) and
thermal satisfaction levels above 98%. This result demonstrates the potential
for deploying such systems in future real-world implementations, enabling
personalised comfort and energy-efficient building operations.
