Towards optimal HVAC control in non-stationary building environments combining active change detection and deep reinforcement learning

Abstract

Energy consumption for heating, ventilation and air conditioning (HVAC) has increased significantly and accounted for a large proportion of building energy growth. Advanced control strategies are needed to reduce energy consumption with maintaining occupant thermal comfort. While compared to other control problems, HVAC control is faced with numerous restrictions from real building environments. One key restriction is non-stationarity, i.e., the varying HVAC system dynamics. Researchers have paid efforts to solve the non-stationarity problems through different approaches, among which deep reinforcement learning gains traction for its advantages in capturing real-time information, controlling adaptively to system feedbacks, avoiding tedious modeling works and combining with deep learning techniques. However, current researches solved non-stationarity in a passive manner which hinders its potential and adds instability in real application. To fill this research gap, we propose a novel HVAC control method combining active building environment change detection and deep Q network (DQN), named non-stationary DQN. This method aims to disentangle the non-stationarity by actively identifying the change points of building environments and learning effective control strategies for corresponding building environments. The simulation results demonstrate that this developed non-stationary DQN method outperforms the state-of-art DQN method in both single-zone control and multi-zone control tasks by saving unnecessary energy use and reducing thermal violation caused by non-stationarity. The improvement can reach 13% in energy-saving and 9% in thermal comfort. Besides, according to the results, our proposed method obtains stability against disturbance and generalization to an unseen building environment, which shows its robustness and potential in real-life applications.