Results show that supervised models outperform LLMs on macro-F1 and precision, while newer LLM generations achieve competitive scores, suggesting that combining them with supervised models may be the best practice.
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.
Weather prediction is a complex forecasting problem because atmospheric variables interact across time and space, observations are heterogeneous, and useful forecasts must remain reliable across different lead times and weather regimes. Numerical weather prediction (NWP) remains a major operational approach, while mach...
Harsh Bariya, S. Degadwala· International Journal of Sci...· 0 citations
This work investigates LLM-based forecasting agents, meaning systems in which a language model contributes to a scored prediction about a future or currently unobserved target, and organizes architectures into three groups.
Xiao-Gang Xu, Jiaqi Tang, Jianmin Chen et al.· 0 citations
Large language models (LLMs) can integrate multisource information and generate structured maintenance recommendations. However, their outputs may underestimate risk and fluctuate over time, reducing the practical executability of decisions. To address these issues, this study proposes a trustworthy LLM-based method fo...
Kai Wang, Meng Yan, Da-Qian Yu et al.· Machines· 0 citations
Road authorities increasingly use machine-learning models to rank high-risk locations and to allocate scarce safety resources. These models are almost always judged by their predictive accuracy on a random hold-out sample. This study shows that practice hides an important risk. We evaluate crash-severity models the way...
Supervised machine learning (ML) methods implicitly assume that their target dataset is error free. For severe weather prediction, point-based observed
Storm Data
storm reports that have been expanded in space and time are commonly-used targets. However, other target datasets could potentially better represent se...
Ryan B. Martz, Eric D. Loken, T. Sandmael et al.· Artificial Intelligence for...· 0 citations
Many studies report forecasting gains for sophisticated machine-learning and deep-learning models without testing whether those gains survive comparison against simple, reproducible benchmarks. This study tests that question for one-step-ahead monthly forecasting of wheat, corn, and soybean prices (FRED, 2000–2024; 300...
S. O. Cirilo, J. M. Vargas-Canales, Dora María Sangerman Jarquín et al.· Forecasting· 0 citations
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