Online Energy Management in Commercial Buildings using Deep Reinforcement Learning

This paper proposes an efficient online approach for reducing energy consumption in large buildings by combining data driven models with deep reinforcement learning techniques. We use data driven methods for modeling the heating and cooling energy consumption in the building. These models are integrated into a single "OpenAI Gym" class in Python to create the environment for studying building energy consumption as a function of control actions, such as setting the discharge temperature set points at different locations in the building. We discuss a policy gradient based actor-critic reinforcement learning approach (Q Actor-Critic) that learns the optimal policy by interacting with the above environment. The optimal policy acts as a controller for adjusting the discharge temperature set point of the dehumidified air in real time so that the total energy consumption can be reduced but the building conditions (temperature and humidity) remain comfortable. Preliminary results show that the method %is fast enough for online application and achieves an energy savings of 2 to 5%.