用大模型零样本预测电网停电风险，效果接近传统方法。
Evaluating Large Language Models for Forced Outage Risk Prediction: Benefits and Comparison to Machine Learning
- 零样本下用大模型分析天气数据预测停电风险
- 监督模型在准确率上更优，但大模型表现接近最新版本
- 大模型优势在于推理可解释性和跨区域适用性
本研究评估大型语言模型（LLMs）在无标注训练数据的零样本框架下，预测德克萨斯中部某电力服务区域因天气导致配电网络强制停运的风险能力。任务为三类预报时间窗口（3小时、6小时、12小时）下的二分类严重性判断，基于六年停电记录与高分辨率气象数据。对比了四种零样本LLMs与两种有监督分类器，输入分别为当前气象观测与气象预报数据。结果表明，有监督模型在宏观F1和精确率上优于LLMs，但新一代LLM已达到具有竞争力的性能。除精度外，LLMs在可操作推理和地理可扩展性方面展现互补优势，提示将二者结合可能是最优实践。
原文摘要 · Abstract (English)
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 for a utility service area in central Texas. Four zero-shot LLMs are benchmarked against two supervised classifiers across two input configurations: one using current weather observations and the other using weather forecast data. Results show that supervised models outperform LLMs on macro-F1 and precision, while newer LLM generations achieve competitive scores. Beyond accuracy, LLMs offer complementary strengths in actionable reasoning and geographic scalability, suggesting that combining them with supervised models may be the best practice.
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