Large Language Model Interface for Home Energy Management Systems

• URL: http://arxiv.org/abs/2501.07919v1
• Date: Tue, 14 Jan 2025 08:10:43 GMT
• Title: Large Language Model Interface for Home Energy Management Systems
• Authors: François Michelon, Yihong Zhou, Thomas Morstyn,
• Abstract summary: Home Energy Management Systems (HEMSs) help households tailor their electricity usage based on power system signals such as energy prices. HEMSs require well-formatted parameterization that reflects the characteristics of the energy resources, houses, and users' needs. We propose an interface that interacts with users to understand and parameterize their badly-formatted answers'', and then outputs well-formatted parameters to implement an HEMS.
• Score: 0.7373617024876725
• License:
• Abstract: 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.

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