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.

翻译：领域专用数字孪生作为智能电网各环节的数字复现体，被预见能够对相应环节进行建模、仿真与控制。与此同时，与人工智能相结合的知识驱动型数字孪生，亦有望通过自然语言交互赋能人类理解系统特性，以支持规划与政策制定。本文首次评估并报告了基于知识驱动的能源数字孪生、在家庭电能计量相关问题上应用检索增强生成（RAG）问答技术的潜力。依托近期发布的、实际构成知识驱动型数字孪生的家庭用电知识图谱，我们研究了ChatGPT、Gemini和Llama模型在回答电力相关问题方面的能力。此外，我们将这些回答与通过现有电力知识数字孪生结合RAG技术生成的答案进行了比较。研究结果表明，RAG方法不仅降低了大型语言模型通常产生错误信息的概率，还通过将回答锚定于可验证数据，显著提升了输出质量。本文详细阐述了研究方法，对比分析了采用与未采用RAG技术的回答差异，并探讨了该发现对人工智能在能源数据分析等专业领域未来应用的启示。