Multi-Space Learning Building Control

原文（中国語）

An approach to optimizing heating, ventilation, and air conditioning (HVAC) temperature setpoints in multi-zone buildings uses a graph-based reinforcement learning framework enhanced with transfer learning. A thermal interaction graph is constructed from spatially distributed building zones, where nodes represent individual rooms or spaces and edges represent thermal or physical relationships. Environmental and operational data, including occupancy, temperature, and weather conditions, are encoded into the graph and processed by a graph neural network to generate a dynamic thermal state representation. A reinforcement learning agent is trained using this representation to learn control policies that adjust HVAC setpoints in real time to minimize energy consumption while maintaining occupant comfort. Transfer learning techniques are employed to adapt pretrained models from one building or zone configuration to another, significantly reducing training time and improving scalability across diverse building types. The system integrates with building management systems (BMS) via programmable interfaces, enabling real-time setpoint optimization.