HVAC-Aware Occupancy Scheduling


  • BoonPing Lim NICTA and Australian National University
  • Menkes van den Briel NICTA and Australian National University
  • Sylvie Thiebaux NICTA and Australian National University
  • Scott Backhaus Los Alamos National Laboratory
  • Russell Bent Los Alamos National Laboratory




Smart buildings, Occupancy scheduling, Mixed integer programming, Large neighborhood search, HVAC control, Planning and scheduling


Energy consumption in commercial and educational buildings is impacted by group activities such as meetings, workshops, classes and exams, and can be reduced by scheduling these activities to take place at times and locations that are favorable from an energy standpoint. This paper improves on the effectiveness of energy-aware room-booking and occupancy scheduling approaches, by allowing the scheduling decisions to rely on an explicit model of the building's occupancy-based HVAC control. The core component of our approach is a mixed-integer linear programming (MILP) model which optimally solves the joint occupancy scheduling and occupancy-based HVAC control problem. To scale up to realistic problem sizes, we embed this MILP model into a large neighbourhood search (LNS). We obtain substantial energy reduction in comparison with occupancy-based HVAC control using arbitrary schedules or using schedules obtained by existing heuristic energy-aware scheduling approaches.




How to Cite

Lim, B., van den Briel, M., Thiebaux, S., Backhaus, S., & Bent, R. (2015). HVAC-Aware Occupancy Scheduling. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9236



Computational Sustainability and Artificial Intelligence