应用微调LLM以减少负荷曲线分析中的数据需求
Applying Fine-Tuned LLMs for Reducing Data Needs in Load Profile Analysis
AI总结：
本文提出一种两阶段微调GPT-3.5的方法，用于电力系统负荷曲线的缺失数据恢复，在减少数据需求的同时达到与BERT-PIN等专用模型相媲美的性能，并显著提升成本与时间效率。
摘要：
本文提出了一种利用微调大型语言模型（LLM）来最小化负荷曲线分析中数据需求的新方法，并通过恢复电力系统负荷曲线中的缺失数据进行了演示。提出了一种两阶段微调策略，以使预训练LLM（即GPT-3.5）适应缺失数据恢复任务。通过实证评估，我们证明了微调模型在准确恢复缺失数据方面的有效性，其性能可与BERT-PIN等最先进的专门设计模型相媲美。主要发现包括提示工程的重要性以及微调样本的最优利用，突出了少样本学习在将知识从一般用户案例迁移到特定目标用户中的效率。此外，与从头训练模型相比，所提方法展现出显著的性价比和时间效率，使其成为数据可用性和计算资源有限场景下的实用解决方案。本研究对其他电力系统负荷曲线分析任务具有巨大的应用潜力。因此，它推进了LLM在电力系统分析中的应用，为提升配电系统的韧性和效率提供了有前景的启示。
英文摘要：
This paper presents a novel method for utilizing fine-tuned Large Language Models (LLMs) to minimize data requirements in load profile analysis, demonstrated through the restoration of missing data in power system load profiles. A two-stage fine-tuning strategy is proposed to adapt a pre-trained LLMs, i.e., GPT-3.5, for missing data restoration tasks. Through empirical evaluation, we demonstrate the effectiveness of the fine-tuned model in accurately restoring missing data, achieving comparable performance to state-of-the-art specifically designed models such as BERT-PIN. Key findings include the importance of prompt engineering and the optimal utilization of fine-tuning samples, highlighting the efficiency of few-shot learning in transferring knowledge from general user cases to specific target users. Furthermore, the proposed approach demonstrates notable cost-effectiveness and time efficiency compared to training models from scratch, making it a practical solution for scenarios with limited data availability and computing resources. This research has significant potential for application to other power system load profile analysis tasks. Consequently, it advances the use of LLMs in power system analytics, offering promising implications for enhancing the resilience and efficiency of power distribution systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
