Multi-Agent Home Energy Management Assistant
On the map: LLM agents and tool use → Financial trading and forecasting agents
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use. Builds on 29 of its 70 references that are in the map. First posted on arXiv 16 Feb 2026.
Builds on
29 of 70 references.
Closest ancestors · 2
Same direction, newest first
- 2025Context-aware LLM-based AI Agents for Human-centered Energy Management Systems in Smart Buildings31 Dec
- 2025Agentic AI Home Energy Management System: A Large Language Model Framework for Residential Load Scheduling30 Oct
Other references · 27
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
- 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
- 2020Scaling Laws for Neural Language Modelscited by 6,411
- 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
- 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
- 2023A Survey of Large Language Modelscited by 2,537
- 2022Emergent Abilities of Large Language Modelscited by 2,483
- 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,205
- 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
- 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
- 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,761
- 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
15 more
- 2023Large Language Models are not Fair Evaluatorscited by 1,050
- 2024Large Language Model based Multi-Agents: A Survey of Progress and Challengescited by 752
- 2023A Comprehensive Overview of Large Language Modelscited by 639
- 2019The many Shapley values for model explanationcited by 341
- 2024Quantifying the Persona Effect in LLM Simulationscited by 164
- 2025A Survey of Context Engineering for Large Language Modelscited by 120
- 2024A Review on Edge Large Language Models: Design, Execution, and Applicationscited by 98
- 2024Evaluating Large Language Models as Generative User Simulators for Conversational Recommendationcited by 45
- 2026Lost in Simulation: LLM-Simulated Users are Unreliable Proxies for Human Users in Agentic Evaluationscited by 36
- 2024History, Development, and Principles of Large Language Models-An Introductory Surveycited by 30
- 2024Agent AI with LangGraph: A Modular Framework for Enhancing Machine Translation Using Large Language Modelscited by 27
- 2024Reliable LLM-based User Simulator for Task-Oriented Dialogue Systemscited by 24
- 2025Large Language Model Interface for Home Energy Management Systemscited by 5
- 2024Large Language Model-Based Interpretable Machine Learning Control in Building Energy Systemscited by 4
- 2024Leveraging Large Language Models for enhanced personalised user experience in Smart Homescited by 4
Built on by
Citing papers per year: 2026: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2026Conversational Demand Response: Bidirectional Aggregator-Prosumer Coordination through Agentic AI
- 2025Conversational Agents for Building Energy Efficiency -- Advising Housing Cooperatives in Stockholm on Reducing Energy Consumption
- 2026SMH-Bench: Benchmarking LLM Agents for Environment-Grounded Reasoning and Action in Smart Homes
- 2024An LLM-Based Digital Twin for Optimizing Human-in-the Loop Systems
- 2026An Autonomous Agent Framework for Feature-Label Extraction from Device Dialogues and Automatic Multi-Dimensional Device Hosting Planning Based on Large Language Models
- 2026HearthNet: Edge Multi-Agent Orchestration for Smart Homes
- 2023Sasha: Creative Goal-Oriented Reasoning in Smart Homes with Large Language Models
- 2024Natural Language Interaction with a Household Electricity Knowledge-based Digital Twin