评估大语言模型在强制停电风险预测中的表现：优势与机器学习对比
原标题：Evaluating Large Language Models for Forced Outage Risk Prediction: Benefits and Comparison to Machine Learning
AI 摘要
本研究评估了大型语言模型（LLM）在零样本框架下预测天气相关强制停电风险的能力，并与监督式机器学习模型进行了对比。实验使用了德克萨斯州中部某公用事业服务区域六年的停电记录和高分辨率天气数据，将问题构建为三个预测时长的二元严重性分类任务。结果显示，监督模型在宏F1和精确度上优于LLM，但新一代LLM取得了有竞争力的分数，并在可操作推理和地理可扩展性方面展现出互补优势，表明将两者结合可能是最佳实践。
正文节选
Evaluating Large Language Models for Forced Outage Risk Prediction: Benefits and Comparison to Machine Learning Abstract This study examines the ability of large language models (LLMs) to predict the risk of weather-related forced outages in the distribution grid in a zero-shot framework, without labeled training data. The problem is formulated as a binary severity classification task across three forecast horizons (3h, 6h, 12h), using six years of outage records and high-resolution weather data