Evaluating Large Language Models for Forced Outage Risk Prediction: Benefits and Comparison to Machine Learning

2026-09-02

AI Summary

研究使用零样本框架下的大型语言模型预测天气相关停电风险，与监督学习模型对比，探索其在准确性和可扩展性上的优势。

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 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.

Problem

• large language models
• forced outages
• weather-related
• zero-shot
• distribution grid

Innovation

• zero-shot framework
• large language models (LLMs)
• weather-related forced outages
• actionable reasoning
• geographic scalability