PROPOSAL OF A HUMAN-CENTRIC AIR CONDITIONING SYSTEM WITH SEAT CHANGES :

We proposed a system that provides seat recommendations or air conditioning feedback based on user reports such “hot” or “cold.” This system was validated through experiments and simulations. As a result, we confirmed that control logic ensuring temperature variation, minimizing seat changes, and considering thermal dynamics significantly improves satisfaction. Additionally, we found that using large language models (LLMs) is crucial for considering various qualitative factors. Based on these insights, we developed a system integrating LLMs and deep reinforcement learning (DRL), which improved overall satisfaction by 31–35% compared to steady-state operation.