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

Summary

This study examines whether large language models (LLMs) can predict weather-related forced outage risk in a zero-shot framework using six years of outage records and high-resolution weather data from a central Texas utility service area. Framing the task as binary severity classification across 3h, 6h, and 12h horizons, the authors benchmark four zero-shot LLMs against two supervised classifiers. Supervised models generally score higher on macro-F1 and precision, while newer LLM generations achieve competitive performance. LLMs further offer complementary strengths in actionable reasoning and geographic scalability, suggesting hybrid approaches may be best.

SourcearXiv Machine LearningAuthor: Christos Petridis, Zoran Obradovic, Mladen Kezunovic
Evaluating Large Language Models for Forced Outage Risk Prediction: Benefits and Comparison to Machine Learning
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[Submitted on 2 Sep 2026]

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

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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.

Subjects:

Machine Learning (cs.LG); Computation and Language (cs.CL)

Cite as: arXiv:2609.04272 [cs.LG]

(or arXiv:2609.04272v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.04272

arXiv-issued DOI via DataCite

Submission history

From: Christos Petridis [view email] [v1] Wed, 2 Sep 2026 19:38:37 UTC (638 KB)

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Key points

  • Four zero-shot LLMs were benchmarked against two supervised classifiers for weather-related forced outage prediction.
  • The study used six years of outage records and high-resolution weather data across central Texas utility areas, with 3h/6h/12h forecast horizons.
  • Supervised models outperformed LLMs on macro-F1 and precision, but newer LLMs achieved competitive scores.
  • LLMs provide complementary benefits such as actionable reasoning and geographic scalability, recommending hybrid model combinations.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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