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.

该研究提出了一种利用经过微调的大型语言模型（LLMs）来最小化负荷数据要求的新方法，并通过在电力系统负荷配置文件中恢复丢失数据来进行验证。通过实证评估，我们展示了经过微调的模型在准确恢复丢失数据方面的有效性，实现了与 BERT-PIN 等专门设计的模型相当的性能。重要发现包括提示工程的重要性和合理利用微调样本，在从普通用户案例向特定目标用户传递知识方面展示了少样本学习的效率。此外，所提出的方法与从头训练模型相比，具有显著的成本效益和时间效率，在数据有限和计算资源有限的情况下，是一种实际可行的解决方案。该研究对于其他电力系统负荷配置文件分析任务具有重要的应用潜力，推动了在电力系统分析中使用 LLMs 的发展，为提高电力分配系统的韧性和效率提供了有希望的启示。