LLMs for Agentic Home Energy Management

Abstract

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/.