The growing complexity in home energy management demands advanced systems that guide occupants toward informed energy decisions. Large language model (LLM)-integrated home energy management systems (HEMS) have shown promise, but prior studies relied on prompt engineering or pre-built platforms with limited customization of agent behavior, or assessed performance through single-turn or -task evaluations. This study introduces a multi-agent home energy management assistant (HEMA), built on LangChain and LangGraph, designed to adaptively and intelligently handle real-world use cases of HEMS with full system customization capability. It carefully classifies user queries via a self-consistency classifier, requests three specialized agents (Analysis, Knowledge, and Control) to prepare accurate, adaptive responses using purpose-built analysis and control tools and retrieval augmented generation under the reasoning and acting mechanism. HEMA was rigorously assessed using two different experimental analyses via an LLM-as-user approach: (1) analytical and informative capabilities using combinatorial test cases of various personas and differing scenarios against three alternative system configurations relying on vanilla LLM and (2) control capabilities using various control scenarios. Out of 295 test cases, HEMA acquired a 91.9% goal achievement rate, successfully fulfilling user requests while providing high levels of factual accuracy, action correctness, interaction quality, and system efficiency, especially when compared to alternative system configurations. Collectively, this study contributes to the advancement of the human-centered design of LLM-integrated HEMS by demonstrating the feasibility and value of agentic architectures, and by clarifying the architectural requirements and evaluation criteria necessary to support adaptive, sustained human-artificial intelligence collaboration in HEMS.

翻译：家庭能源管理日益增长的复杂性要求先进系统能够引导居住者做出明智的能源决策。集成大语言模型（LLM）的家庭能源管理系统（HEMS）已展现出潜力，但先前研究依赖于提示工程或预构建平台，其智能体行为定制能力有限，或通过单轮或单任务评估来评估性能。本研究引入了一种基于LangChain和LangGraph构建的多智能体家庭能源管理助手（HEMA），旨在以完全的系统定制能力自适应且智能地处理HEMS的现实用例。它通过自洽分类器仔细分类用户查询，在推理与行动机制下，请求三个专业智能体（分析、知识与控制）利用专门构建的分析与控制工具及检索增强生成技术，准备准确、自适应的响应。通过采用LLM-as-user方法，本研究通过两种不同的实验分析对HEMA进行了严格评估：（1）针对依赖原始LLM的三种替代系统配置，使用不同人物角色和场景的组合测试案例评估其分析和信息提供能力；（2）使用各种控制场景评估其控制能力。在295个测试案例中，HEMA实现了91.9%的目标达成率，成功满足用户请求，同时提供了高水平的事实准确性、行动正确性、交互质量和系统效率，尤其是在与替代系统配置相比时。总体而言，本研究通过展示智能体架构的可行性与价值，并阐明支持HEMS中自适应、持续的人机协作所需的架构要求与评估标准，为推进以人为中心的LLM集成HEMS设计做出了贡献。