Natural Language Interaction with a Household Electricity Knowledge-based Digital Twin

• URL: http://arxiv.org/abs/2406.06566v4
• Date: Fri, 16 Aug 2024 07:43:55 GMT
• Title: Natural Language Interaction with a Household Electricity Knowledge-based Digital Twin
• Abstract summary: Domain specific digital twins, representing a digital replica of various segments of the smart grid, are foreseen as able to model, simulate, and control the respective segments. This paper is the first to assess and report on the potential of Retrieval Augmented Generation (RAG) question answers.
• Score: 0.196629787330046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain specific digital twins, representing a digital replica of various segments of the smart grid, are foreseen as able to model, simulate, and control the respective segments. At the same time, knowledge-based digital twins, coupled with AI, may also empower humans to understand aspects of the system through natural language interaction in view of planning and policy making. This paper is the first to assess and report on the potential of Retrieval Augmented Generation (RAG) question answers related to household electrical energy measurement aspects leveraging a knowledge-based energy digital twin. Relying on the recently published electricity consumption knowledge graph that actually represents a knowledge-based digital twin, we study the capabilities of ChatGPT, Gemini and Llama in answering electricity related questions. Furthermore, we compare the answers with the ones generated through a RAG techniques that leverages an existing electricity knowledge-based digital twin. Our findings illustrate that the RAG approach not only reduces the incidence of incorrect information typically generated by LLMs but also significantly improves the quality of the output by grounding responses in verifiable data. This paper details our methodology, presents a comparative analysis of responses with and without RAG, and discusses the implications of our findings for future applications of AI in specialized sectors like energy data analysis.

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