Existing home energy management systems conceptualize occupants as passive recipients of energy information and control, which limits their ability to effectively support informed decision-making and sustained engagement. This paper presents Home Energy Management Assistant (HEMA), the first open-source, multi-agent system enabling sustained human-AI collaboration - multi-turn conversational interactions with preserved context - across diverse home energy management (HEM) tasks - from energy analysis and educational support to smart device control. HEMA combines large language model (LLM) reasoning capabilities with 36 purpose-built domain-specific tools through a three-layer architecture: a web-based conversational interface, a backend API server, and a multi-agent system. The system features three specialized agents - Analysis (energy consumption patterns and cost optimization), Knowledge (educational queries and rebate information), and Control (smart device management and scheduling) - coordinated through a self-consistency classifier that routes user queries using chain-of-thought reasoning. This architecture enables various energy analyses, adaptive explanations, and streamlined device control. HEMA also includes a comprehensive evaluation framework using an LLM-as-simulated-user methodology with 23 objective metrics across task performance, factual accuracy, interaction quality, and system efficiency, allowing systematic testing across diverse scenarios and user personas without requiring extensive human subject testing. Through demonstrations using real-world household energy consumption data, we show how HEMA supports informed decision-making and active engagement in HEM, highlighting its potential as a user-friendly, adaptable tool for residential deployment and as a research platform for HEM innovation.

翻译：现有家庭能源管理系统将用户视为能源信息和控制的被动接受者，这限制了其有效支持知情决策和持续参与的能力。本文提出了家庭能源管理助手（Home Energy Management Assistant, HEMA），这是首个开源多智能体系统，能够实现跨多种家庭能源管理任务的持续人机协作——从能源分析与教育支持到智能设备控制，并支持保留上下文的多次对话交互。HEMA通过三层架构将大型语言模型的推理能力与36个专用领域工具相结合：基于Web的对话界面、后端API服务器和多智能体系统。该系统包含三个专门化的智能体——分析智能体（能耗模式与成本优化）、知识智能体（教育查询与返利信息）和控制智能体（智能设备管理与调度）——并通过基于链式推理路由用户查询的自一致性分类器进行协调。该架构支持多种能源分析、自适应解释和简化的设备控制。HEMA还包含一个综合评估框架，采用LLM模拟用户方法，涵盖任务性能、事实准确性、交互质量和系统效率四个维度的23个客观指标，可在无需大规模人类受试者测试的情况下，针对不同场景和用户画像进行系统性测试。通过使用真实家庭能耗数据的演示，我们展示了HEMA如何支持家庭能源管理中的知情决策和主动参与，凸显其作为住宅部署的友好适应性工具和家庭能源管理创新研究平台的潜力。