  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.
