Energy saving optimization method for building HVAC based on PPO and human guidance

There is a natural contradiction between energy efficiency and comfort in building HVAC systems. This paper proposes an Augmented Intelligence Proximal Policy Optimization (AuI-PPO) control method that combines human guidance. In system modeling, indoor and outdoor temperatures, electricity prices, and human intervention signals are introduced as states, and actions are defined as setting temperature adjustments. The reward function integrates energy consumption costs and PMV comfort. The experiment is based on the TRNSYS simulation platform and compares different building areas (60, 120, 180 m2) with climatic conditions. The results indicate that AuI-PPO outperforms PPO, TD3, HI-DDPG, and DDPG in terms of convergence speed and stability, with a final R2 of over 0.95 and achieving the lowest average PMV and energy consumption load. Sensitivity analysis shows that the setting with a learning rate of 0.001, a hidden layer of 512, and τ = 1e-4 yields the best results. Overall, AuI-PPO can effectively reduce costly exploration, strike a balance between energy efficiency and comfort, and provide a feasible solution for optimizing energy efficiency in smart buildings.