# Agentic AI Home Energy Management System: A Large Language Model Framework for Residential Load Scheduling

arXiv: https://arxiv.org/abs/2510.26603 · HTML: https://mlsanity.com/paper/2510.26603/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 15 of its 35 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 15 of 15)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [gpt-oss-120b & gpt-oss-20b Model Card](https://mlsanity.com/paper/2508.10925/) (2025)
- [Prompt Injection attack against LLM-integrated Applications](https://mlsanity.com/paper/2306.05499/) (2023)
- [Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions](https://mlsanity.com/paper/2503.23278/) (2025)
- [AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges](https://mlsanity.com/paper/2505.10468/) (2025)
- [From Language to Action: A Review of Large Language Models as Autonomous Agents and Tool Users](https://mlsanity.com/paper/2508.17281/) (2025)
- [Exploring the Capabilities and Limitations of Large Language Models in the Electric Energy Sector](https://mlsanity.com/paper/2403.09125/) (2024)
- [WaferLLM: Large Language Model Inference at Wafer Scale](https://mlsanity.com/paper/2502.04563/) (2025)
- [Large Language Model Interface for Home Energy Management Systems](https://mlsanity.com/paper/2501.07919/) (2025)
- [Generating HomeAssistant Automations Using an LLM-based Chatbot](https://mlsanity.com/paper/2505.02802/) (2025)
- [Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehicles](https://mlsanity.com/paper/2501.15544/) (2025)
- [Can AI Make Energy Retrofit Decisions? An Evaluation of Large Language Models](https://mlsanity.com/paper/2509.06307/) (2025)

## Built on by (most cited first, 8 of 8)

- [Agentic AI Empowered Intent-Based Networking for 6G](https://mlsanity.com/paper/2601.06640/) (2026)
- [Multi-Agent Home Energy Management Assistant](https://mlsanity.com/paper/2602.15219/) (2026)
- [Conversational Demand Response: Bidirectional Aggregator-Prosumer Coordination through Agentic AI](https://mlsanity.com/paper/2603.06217/) (2026)
- [LLMs and Agentic AI Systems for Smart Grids: A Tutorial on Architectures and Applications](https://mlsanity.com/paper/2607.18147/) (2026)
- [Engineering Trustworthy Agentic AI for Critical Systems](https://mlsanity.com/paper/2607.18548/) (2026)
- [EnergyBridge: Benchmarking Household Energy Management, User Participation, and Grid Flexibility](https://mlsanity.com/paper/2608.08691/) (2026)
- [BuildOcc: A Large Language Model Occupant Agent Platform for Building Energy Research](https://mlsanity.com/paper/2609.02729/) (2026)
- [Energy Vision--Language--Action: A Controlled Multimodal Benchmark for Intent-Conditioned Residential Energy Management](https://mlsanity.com/paper/2609.31648/) (2026)

## Abstract (arXiv)

The electricity sector transition requires substantial increases in residential demand response capacity, yet Home Energy Management Systems (HEMS) adoption remains limited by user interaction barriers requiring translation of everyday preferences into technical parameters. While large language models have been applied to energy systems as code generators and parameter extractors, no existing implementation deploys LLMs as autonomous coordinators managing the complete workflow from natural language input to multi-appliance scheduling. This paper presents an agentic AI HEMS where LLMs autonomously coordinate multi-appliance scheduling from natural language requests to device control, achieving optimal scheduling without example demonstrations. A hierarchical architecture combining one orchestrator with three specialist agents uses the ReAct pattern for iterative reasoning, enabling dynamic coordination without hardcoded workflows while integrating Google Calendar for context-aware deadline extraction. Evaluation across three open-source models using real Austrian day-ahead electricity prices reveals substantial capability differences. Llama-3.3-70B successfully coordinates all appliances across all scenarios to match cost-optimal benchmarks computed via mixed-integer linear programming, while other models achieve perfect single-appliance performance but struggle to coordinate all appliances simultaneously. Progressive prompt engineering experiments demonstrate that analytical query handling without explicit guidance remains unreliable despite models' general reasoning capabilities. We open-source the complete system including orchestration logic, agent prompts, tools, and web interfaces to enable reproducibility, extension, and future research.