# Applying Fine-Tuned LLMs for Reducing Data Needs in Load Profile Analysis

arXiv: https://arxiv.org/abs/2406.02479 · HTML: https://mlsanity.com/paper/2406.02479/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 48 references that are in the map.
- First posted on arXiv 2 Jun 2024.

## Builds on (most cited first, 13 of 13)

- [Teaching Large Language Models to Self-Debug](https://mlsanity.com/paper/2304.05128/) (2023)
- [GAIN: Missing Data Imputation using Generative Adversarial Nets](https://mlsanity.com/paper/1806.02920/) (2018)
- [Mathematical Capabilities of ChatGPT](https://mlsanity.com/paper/2301.13867/) (2023)
- [FinGPT: Open-Source Financial Large Language Models](https://mlsanity.com/paper/2306.06031/) (2023)
- [Data-efficient Fine-tuning for LLM-based Recommendation](https://mlsanity.com/paper/2401.17197/) (2024)
- [Large Language Model-Aware In-Context Learning for Code Generation](https://mlsanity.com/paper/2310.09748/) (2023)
- [AceCoder: Utilizing Existing Code to Enhance Code Generation](https://mlsanity.com/paper/2303.17780/) (2023)
- [ChatGPT and Other Large Language Models for Cybersecurity of Smart Grid Applications](https://mlsanity.com/paper/2311.05462/) (2023)
- [MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations](https://mlsanity.com/paper/2210.01167/) (2022)
- [Applying Large Language Models to Power Systems: Potential Security Threats](https://mlsanity.com/paper/2311.13361/) (2023)
- [Load Profile Inpainting for Missing Load Data Restoration and Baseline Estimation](https://mlsanity.com/paper/2211.16332/) (2022)
- [BERT-PIN: A BERT-based Framework for Recovering Missing Data Segments in Time-series Load Profiles](https://mlsanity.com/paper/2310.17742/) (2023)
- [Risks of Practicing Large Language Models in Smart Grid: Threat Modeling and Validation](https://mlsanity.com/paper/2405.06237/) (2024)

## Built on by (most cited first, 2 of 2)

- [The Personalization Paradox: Semantic Loss vs. Reasoning Gains in Agentic AI Q&A](https://mlsanity.com/paper/2512.04343/) (2025)
- [Agentic AI for operating scientific instruments for nanoscale characterization](https://mlsanity.com/paper/2608.26198/) (2026)

## Abstract (arXiv)

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.