# Large Language Model Interface for Home Energy Management Systems

arXiv: https://arxiv.org/abs/2501.07919 · HTML: https://mlsanity.com/paper/2501.07919/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 14 Jan 2025.

## Builds on (most cited first, 8 of 8)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [Exploring the Capabilities and Limitations of Large Language Models in the Electric Energy Sector](https://mlsanity.com/paper/2403.09125/) (2024)
- [Large Foundation Models for Power Systems](https://mlsanity.com/paper/2312.07044/) (2023)
- [Comparative Analysis of Word Embeddings for Capturing Word Similarities](https://mlsanity.com/paper/2005.03812/) (2020)
- [Enabling Large Language Models to Perform Power System Simulations with Previously Unseen Tools: A Case of Daline](https://mlsanity.com/paper/2406.17215/) (2024)

## Built on by (most cited first, 6 of 6)

- [Agentic AI Home Energy Management System: A Large Language Model Framework for Residential Load Scheduling](https://mlsanity.com/paper/2510.26603/) (2025)
- [Context-aware LLM-based AI Agents for Human-centered Energy Management Systems in Smart Buildings](https://mlsanity.com/paper/2512.25055/) (2025)
- [Multi-Agent Home Energy Management Assistant](https://mlsanity.com/paper/2602.15219/) (2026)
- [Large Language Model-Empowered Interactive Load Forecasting](https://mlsanity.com/paper/2505.16577/) (2025)
- [Conversational Demand Response: Bidirectional Aggregator-Prosumer Coordination through Agentic AI](https://mlsanity.com/paper/2603.06217/) (2026)
- [Energy Vision--Language--Action: A Controlled Multimodal Benchmark for Intent-Conditioned Residential Energy Management](https://mlsanity.com/paper/2609.31648/) (2026)

## Abstract (arXiv)

Home Energy Management Systems (HEMSs) help households tailor their electricity usage based on power system signals such as energy prices. This technology helps to reduce energy bills and offers greater demand-side flexibility that supports the power system stability. However, residents who lack a technical background may find it difficult to use HEMSs effectively, because HEMSs require well-formatted parameterization that reflects the characteristics of the energy resources, houses, and users' needs. Recently, Large-Language Models (LLMs) have demonstrated an outstanding ability in language understanding. Motivated by this, we propose an LLM-based interface that interacts with users to understand and parameterize their ``badly-formatted answers'', and then outputs well-formatted parameters to implement an HEMS. We further use Reason and Act method (ReAct) and few-shot prompting to enhance the LLM performance. Evaluating the interface performance requires multiple user--LLM interactions. To avoid the efforts in finding volunteer users and reduce the evaluation time, we additionally propose a method that uses another LLM to simulate users with varying expertise, ranging from knowledgeable to non-technical. By comprehensive evaluation, the proposed LLM-based HEMS interface achieves an average parameter retrieval accuracy of 88%, outperforming benchmark models without ReAct and/or few-shot prompting.