Heating, ventilation, and air-conditioning (HVAC) system’s supervisory control is crucial for energy-efficient thermal comfort in buildings. The control logic is usually specified as ‘if-then-that-else’ rules that capture the domain expertise of HVAC operators, but they often have conflicts that may lead to sub-optimal HVAC performance. We propose EImprove, a reinforcement-learning (RL) based framework that exploits these conflicts to learn a resolution policy. We evaluate EImprove through a co-simulation strategy involving EnergyPlus simulations of a real-world office setting and a formal requirement specifier. Our experiments show that EImprove learns 75% faster than a pure RL framework.

Increasing real estate and other infrastructure costs have resulted in the trend of co-working offices where users pay as they use for individual desks. Co-working offices that provide personalized comfort need to address users with potentially widely varying thermal comfort preferences. Providing personalized comfort in cabins separated by physical partitions with neighboring thermal zones or open-plan offices with a single actuator has received attention in the literature. In this article, the problem of minimizing user discomfort in open-plan co-working offices with multiple actuators while being cognizant of the energy consumed is considered. Specifically, the decision problems of assigning users to desks based on their thermal preferences and jointly controlling the multiple actuators are addressed. The non-linearities in the underlying thermodynamic constraints and the seating decision together make the problem computationally hard. A two-step heuristic that addresses these issues is presented. First, using a model that accounts for spatio-temporal thermodynamics, a one-time assignment of users to desks is performed that reduces the thermal resistance faced by the HVAC systems to provide the preferred comfort levels. Next, the setpoints are decided for all actuators to jointly minimize user discomfort by optimization and model-predictive control. Further, scalability is addressed by clustering user preferences and the associated HVAC actuators’ setpoints for the cases where a large number of actuators may be present in the room.