AI 中文总结
研究家庭能源管理系统中，大语言模型智能体能否为多设备家庭能源调度提供自然语言接口。通过工具调用ReAct智能体及相关数据，对三个商业模型进行基准测试，结果显示模型表现各异，能实现高调度成功率和接近最优性。
AI 中文摘要
家庭能源管理系统（HEMS）可降低住宅用电成本并支持需求响应，但因难以将家庭偏好转化为技术调度约束，采用受限。本文评估大语言模型（LLM）智能体能否为多设备家庭能源调度提供实用自然语言接口。提出工具调用ReAct智能体，用多种数据针对混合整数线性规划（MILP）基准调度灵活负载。对三个商业模型在多种场景下进行基准测试，结果表明……
英文摘要
Home Energy Management Systems (HEMS) can reduce residential electricity costs, but many require users to express everyday preferences as technical constraints. This paper presents a tool-calling ReAct agent that converts natural-language requests into schedules for multiple household appliances using half-hourly Octopus Agile prices, weather forecasts, photovoltaic generation estimates, and household demand data. Five large language model backends are evaluated against a mixed-integer linear programming benchmark across dynamic tariff conditions, constraint conflicts, weather-aware scheduling, and a seven-day rolling deployment. Native function calling achieves high scheduling success and near-optimal cost on ordinary tariff days, whereas text-parsed actions reduce reliability. Constraint-conflict testing shows that low cost does not guarantee safe or feasible behaviour. Claude Sonnet 4.6 performs best in power-cap and infeasibility scenarios, while Qwen-3 achieves higher overall constraint compliance than GPT-4o-mini. The evaluation also identifies fabricated schedules, failed commitments, and reasoning-to-action failures in which models explain a deadline correctly but commit an invalid schedule. Weather-aware scheduling reduces cost and increases solar self-consumption under overcast conditions, but provides limited or adverse economic value under some dynamic-price regimes. Across the evaluated seven-day period, the agents capture 96.7-98.0% of the savings available between an off-peak timer and the MILP oracle and outperform the rule-based policies. The results support LLM-based HEMS orchestration, provided that every committed schedule is checked by an independent deterministic feasibility validator before actuation. Code and a live demonstration are available at https://github.com/sokistar24/ecohome-experiments and https://www.ecohomeagent.com/.
Comments15 pages, 8 figures
