Abstract
Accurate occupancy information is essential for closed-loop occupant-centric control (OCC) in smart buildings. However, existing vision-based occupancy measurement methods often struggle to provide stable and accurate measurements in real indoor environments, and their implications for downstream HVAC control remain insufficiently studied. To achieve Net Zero emissions by 2050, this paper presents an experimental study of large language models (LLMs)-enhanced vision-based indoor occupancy measurement and its impact on OCC-enabled HVAC operation. Detection-only, tracking-based, and LLM-based refinement pipelines are compared under identical conditions using real surveillance data collected from a research laboratory in China, with frame-level manual ground-truth annotations. Results show that tracking-based methods improve temporal stability over detection-only measurement, while LLM-based refinement further improves occupancy measurement performance and reduces false unoccupied prediction. The best-performing pipeline, YOLOv8+DeepSeek, achieves an accuracy of 0.8824 and an F1-score of 0.9320. This pipeline is then integrated into an HVAC supervisory model predictive control framework in OpenStudio-EnergyPlus. Experimental results demonstrate that the proposed framework can support more efficient OCC operation, achieving a substantial HVAC energy-saving potential of 17.94%. These findings provide an effective methodology and practical foundation for future research in AI-enhanced smart building operations.
Abstract (translated)
准确的 occupancy 信息对于智能建筑中以 occupant 为中心的闭环控制（OCC）至关重要。然而，现有的基于视觉的 occupancy 测量方法在实际室内环境中往往难以提供稳定且精确的测量结果，且其对下游 HVAC 控制的影响尚未得到充分研究。为实现 2050 年净零排放目标，本文提出了一项实验研究，探讨大语言模型（LLMs）增强的基于视觉的室内 occupancy 测量方法及其对支持 OCC 的 HVAC 运行的影响。研究在相同条件下，使用来自中国某研究实验室的真实监控数据（包含帧级人工标注的真值）对比了仅检测、基于跟踪以及基于 LLM 优化的三种流程。结果表明，基于跟踪的方法相较于仅检测测量提升了时间稳定性，而基于 LLM 的优化进一步提高了 occupancy 测量性能并减少了误报“无人”的情况。表现最佳的流程 YOLOv8+DeepSeek 达到了 0.8824 的准确率和 0.9320 的 F1 分数。随后，将该流程集成至 OpenStudio-EnergyPlus 中的 HVAC 监督模型预测控制（MPC）框架内。实验结果表明，所提出的框架能够支持更高效的 OCC 运行，实现了高达 17.94% 的 HVAC 节能潜力。这些发现为未来 AI 增强型智能建筑运行研究提供了有效的方法论和实用基础。
URL
https://arxiv.org/abs/2603.26081
