Intelligent HVAC Control System Based on Large Language Models: Practice of Integrating AI and Reinforcement Learning

This project presents an intelligent HVAC control system integrating the Qwen-72B large model and reinforcement learning, aiming to optimize indoor comfort and energy efficiency. Built on the OpenEnv framework, the project constructs a standardized reinforcement learning environment and explores a new paradigm of AI-driven building energy efficiency management. The current implementation is an environment framework and SDK demonstration, which can be extended to multi-scenario applications in the future.

Traditional HVAC systems account for 40%-60% of a building's total energy consumption, with rigid strategies that struggle to adapt to complex environments; reinforcement learning has great potential in HVAC optimization, but faces challenges such as reward function design and high-dimensional state space processing. This project innovatively introduces the Qwen-72B large model as the "brain" of HVAC control to explore a new paradigm.

The project is built on the OpenEnv framework (standardized interfaces, containerized deployment, Web API support, cloud integration). The system architecture includes: action space (natural language control commands), observation space (message echo, length, reward, etc.), and reward function (currently a placeholder of message length ×0.1). The client SDK supports three modes: Docker, direct connection, and context manager.

As a decision-making agent, Qwen-72B has three key features: 1. Natural language interface (understanding states and generating interpretable control commands); 2. Knowledge injection (utilizing pre-trained knowledge such as thermodynamics and comfort models); 3. Context learning (quickly adapting to specific buildings and user preferences through few-shot prompting).

The server supports WebSocket concurrency (up to 4 sessions, suitable for multi-zone control); it supports one-click deployment to Hugging Face Spaces, which provides Web interaction interface, API documentation, health check, WebSocket communication and other functions after deployment.

The current implementation is mainly an environment framework and SDK demonstration; the core LLM control logic (the actual decision-making process of Qwen-72B) has not been fully disclosed. Further implementations are needed: actual HVAC control logic, integration with real systems, and a complete RL training process.

The project can be expanded to the following scenarios in the future: multi-zone collaborative control, predictive maintenance, personalized comfort optimization, demand response and grid interaction, natural language interaction interface, etc.

This project is a cutting-edge exploration of AI in the field of building energy conservation. By combining the understanding ability of LLM and the decision optimization ability of RL, it is expected to develop a more intelligent, efficient, and interpretable building energy management system. Although it is in the early stage, it provides a valuable starting point and experimental platform for research and development. With technological progress in the future, the vision of intelligent buildings will be gradually realized.