Adaptive Lighting and Thermal Comfort Control Strategies in Digital Twin Classroom via Deep Reinforcement Learning

With the advancement of smart education and carbon neutrality goals, optimizing Indoor Environmental Quality (IEQ) while minimizing energy consumption is critical. Traditional PID or rule-based strategies struggle with the strong non-linearity and time delays of photothermal coupling in high-density classrooms. This paper proposes an adaptive closed-loop control framework fusing Digital Twin (DT) and Deep Reinforcement Learning (DRL). A high-fidelity multi-physics model is constructed as a virtual testbed, utilizing the Proximal Policy Optimization (PPO) algorithm to learn multi-objective strategies. The trained agent is deployed to an edge gateway for real-time inference. Experimental results from a field study distinguish this work from pure simulations. Results demonstrate that compared to PID baselines, the proposed strategy reduces energy consumption by 28.4% while maintaining thermal comfort (PMV) and visual comfort compliance. Furthermore, the variance of PMV is reduced by 66.7%, and system recovery time under stochastic disturbances is shortened by 31.4%.