Explainable Physically Constrained Graph Neural Control and Multi-objective Reinforcement Learning for User-Centric HVAC Managem

Heating, ventilation, and air conditioning (HVAC) systems account for an important portion of building energy consumption, usually representing 30\\% to 50\\% of the total building energy use; optimizing them is critical for achieving the \u201cdual-carbon\u201d targets and for improving user comfort. Existing control methods based on deep reinforcement learning have achieved some success in discrete scenarios, but they often display poor generalization when faced with different building thermodynamic characteristics and user preferences, and they lack multi-objective optimization and interpretability. This paper proposes a user\u2011centric explainable physically constrained graph neural network control framework. The framework constructs a graph neural network by utilizing the building topology, embeds thermodynamic conservation constraints into the controller, and, in combination with multi\u2011objective reinforcement learning methods, seeks a Pareto\u2011optimal balance among energy consumption, user comfort, and carbon emissions. Experiments were conducted in a multi\u2011zone building simulation environment, where data collected from real settings with synthetic noise were used to train the model, and comparisons were made with baseline methods such as physically constrained deep learning models, traditional PID control, and multi\u2011agent RL. Experimental results show that the proposed method can reduce energy consumption by about 18\\%, reduce comfort violation rates by 45\\%, and exhibit better generalization in scenarios beyond the testing conditions. By adopting explanatory algorithms, the influence of factors such as outdoor temperature and occupancy on the policy is revealed, thereby enhancing the transparency and reliability of the control process.