Zhong, Dianyu; Xing, Tian; Sun, Kailai; Zhang, Ziyou; Zhao, Qianchuan; Kang, Jian; (2025) Topology-Aware Hypergraph Reinforcement Learning for Indoor Occupant-Centric HVAC Control. Energy and Buildings , Article 116219. 10.1016/j.enbuild.2025.116219.

Text Kailai Sun-OCC_RL_.pdf - Accepted Version Access restricted to UCL open access staff until 12 August 2026. Download (1MB)

Abstract

Heating, ventilation, and air conditioning (HVAC) systems account for a significant portion of global energy consumption, posing a significant obstacle to achieving Net Zero emissions by 2050. Existing reinforcement learning (RL) based occupant-centric control strategies show promise but face limitations in real-world settings due to insufficient integration of occupant activity data and underutilization of building spatial structures. To address these challenges, this study introduces a novel framework that integrates real-time, vision-based occupant activity recognition and models the building’s spatial topology as a hypergraph, enabling topology-aware value decomposition. Our approach outperforms benchmark RL algorithms, achieving state-of-the-art performance. Experiments based on real-world office data demonstrate that integrating occupant activity reduces the predicted percentage of dissatisfied by 20.9 % and improves energy efficiency by 21.1 %; separately, leveraging building topology yields a 25.6 % reduction and a 13.7 % efficiency gain. These findings offer new insights into intelligent control for energy-efficient, occupant-centric buildings and confirm the framework’s potential for large-scale, real-world deployment.

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