用少量数据让大模型预测电价高峰日，效果不输传统方法。
A Few-Shot LLM Framework for Extreme Day Classification in Electricity Markets
- 将电力系统状态转为自然语言提示，输入大模型进行少样本分类
- 在德州市场数据上表现媲美支持向量机和XGBoost，数据少时更优
- 适合数据稀缺的电力市场预测场景，无需大量历史标注数据
本文提出一种基于大语言模型（LLM）的少样本分类框架，用于预测未来一天是否会出现实时电价高峰。该方法整合电力需求、可再生能源发电、天气预报及近期电价等系统状态信息，提取为统计特征，并转化为自然语言提示输入LLM，结合通用指令判断次日为高电价日的可能性并输出置信度分数。基于德克萨斯州电力市场历史数据的实验表明，该少样本方法性能可媲美支持向量机（SVM）和XGBoost等监督学习模型，在历史数据有限的情况下甚至表现更优。结果表明，LLM可作为数据高效工具，适用于标注数据稀缺的电价高峰分类任务。
原文摘要 · Abstract (English)
This paper proposes a few-shot classification framework based on Large Language Models (LLMs) to predict whether the next day will have spikes in real-time electricity prices. The approach aggregates system state information, including electricity demand, renewable generation, weather forecasts, and recent electricity prices, into a set of statistical features that are formatted as natural-language prompts and fed to an LLM along with general instructions. The model then determines the likelihood that the next day would be a spike day and reports a confidence score. Using historical data from the Texas electricity market, we demonstrate that this few-shot approach achieves performance comparable to supervised machine learning models, such as Support Vector Machines and XGBoost, and outperforms the latter two when limited historical data are available. These findings highlight the potential of LLMs as a data-efficient tool for classifying electricity price spikes in settings with scarce data.
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