Work

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

Year: 2026

Type: article

Abstract: 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%.

Source: Electronics

DOI: 10.3390/electronics15040873

Authors: Xuegang Wu, Pinle Qin

Institution: North University of China

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Quality education, Industry, innovation and infrastructure, Climate action

Open Access status: gold

Estimated APC paid: $2,896