用大模型让建筑能源系统听懂人话，自动调设备省电省钱。
Context-aware LLM-based AI Agents for Human-centered Energy Management Systems in Smart Buildings
- 构建感知-决策-执行闭环，通过自然语言交互实现智能能源管理。
- 设备控制准确率达86%，记忆任务97%，但复杂成本估算仅49%。
- 适合关注智能楼宇、人机交互的科研与工程人员参考。
本研究提出一种基于大语言模型（LLM）的建筑能源管理系统（BEMS）AI代理的概念框架与原型评估，通过自然语言交互实现智能建筑中的上下文感知能源管理。该框架包含感知（传感）、中央控制（大脑）和执行（驱动与用户交互）三个模块，形成闭环反馈回路，用于采集、分析和解释能源数据，以智能响应用户查询并管理连接设备。借助LLM的自主数据分析能力，该代理旨在提供能耗洞察、成本预测与设备调度等上下文感知服务，克服现有系统的局限性。在四个真实住宅能源数据集上，使用120个用户查询进行评估，涵盖延迟、功能、能力、准确率和成本效益等指标。通过方差分析验证了框架的泛化能力。结果显示：设备控制准确率为86%，记忆相关任务达97%，调度自动化为74%，能源分析为77%，而复杂成本估算准确率仅为49%。该基准研究推动了对基于LLM的BEMS AI代理的系统评估，并指明了响应准确率与计算效率间的权衡关系。
原文摘要 · Abstract (English)
This study presents a conceptual framework and a prototype assessment for Large Language Model (LLM)-based Building Energy Management System (BEMS) AI agents to facilitate context-aware energy management in smart buildings through natural language interaction. The proposed framework comprises three modules: perception (sensing), central control (brain), and action (actuation and user interaction), forming a closed feedback loop that captures, analyzes, and interprets energy data to respond intelligently to user queries and manage connected appliances. By leveraging the autonomous data analytics capabilities of LLMs, the BEMS AI agent seeks to offer context-aware insights into energy consumption, cost prediction, and device scheduling, thereby addressing limitations in existing energy management systems. The prototype's performance was evaluated using 120 user queries across four distinct real-world residential energy datasets and different evaluation metrics, including latency, functionality, capability, accuracy, and cost-effectiveness. The generalizability of the framework was demonstrated using ANOVA tests. The results revealed promising performance, measured by response accuracy in device control (86%), memory-related tasks (97%), scheduling and automation (74%), and energy analysis (77%), while more complex cost estimation tasks highlighted areas for improvement with an accuracy of 49%. This benchmarking study moves toward formalizing the assessment of LLM-based BEMS AI agents and identifying future research directions, emphasizing the trade-off between response accuracy and computational efficiency.
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