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View a PDF of the paper titled Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework, by Mengshuo Jia and 2 other authors
Abstract:The integration of experimental technologies with large language models (LLMs) is transforming scientific research. It positions AI as a versatile research assistant rather than a mere problem-solving tool. In the field of power systems, however, managing simulations — one of the essential experimental technologies — remains a challenge for LLMs due to their limited domain-specific knowledge, restricted reasoning capabilities, and imprecise handling of simulation parameters. To address these limitations, this paper proposes a feedback-driven, multi-agent framework. It incorporates three proposed modules: an enhanced retrieval-augmented generation (RAG) module, an improved reasoning module, and a dynamic environmental acting module with an error-feedback mechanism. Validated on 69 diverse tasks from Daline and MATPOWER, this framework achieves success rates of 93.13% and 96.85%, respectively. It significantly outperforms ChatGPT 4o, o1-preview, and the fine-tuned GPT-4o, which all achieved a success rate lower than 30% on complex tasks. Additionally, the proposed framework also supports rapid, cost-effective task execution, completing each simulation in approximately 30 seconds at an average cost of 0.014 USD for tokens. Overall, this adaptable framework lays a foundation for developing intelligent LLM-based assistants for human researchers, facilitating power system research and beyond.
Submission history
From: Mengshuo Jia [view email]
[v1]
Thu, 21 Nov 2024 19:01:07 UTC (2,017 KB)
[v2]
Tue, 15 Apr 2025 19:33:50 UTC (4,432 KB)
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#Enhancing #LLMs #Power #System #Simulations #Feedbackdriven #Multiagent #Framework
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