Evaluating Large Language Models for Forced Outage Risk Prediction: Benefits and Comparison to Machine Learning
Christos Petridis, Zoran Obradovic, Mladen Kezunovic
cs.LG · cs.CL
Quick verdict
Zero-shot LLMs trail supervised ML on weather-driven outage risk (F1 gap 0.04-0.13); no CIs, untested transferability claims temper the hybrid pitch.
Summary
The paper asks whether zero-shot large language models can predict weather-driven forced outages in a distribution grid without any labeled training data. Using six years (2018-2023) of feeder-level outage records from a central Texas utility and hourly Open-Meteo reanalysis weather over a 10x10 grid, the task is framed as binary severity classification at 3h, 6h, and 12h horizons, with thresholds (5, 8, and 10 outages) set to a ~5% positive rate; February 2021 (Winter Storm Uri) is excluded. Four GPT models (4o, 4o-mini, 4.1, 4.1-mini) receive either current observations or window-aggregated forecasts and answer with a probability binarized at a fixed, untuned 0.5, benchmarked against Logistic Regression and LightGBM under Leave-One-Year-Out cross-validation. Supervised models win on macro-F1 and precision in every setting: with forecasts, LightGBM reaches 0.74 macro-F1 at 3h versus 0.61 for the best LLM (overall gap 0.04-0.13). GPT-4.1-family models are stable across horizons; GPT-4o over-predicts (recall 0.82, precision 0.14 at 12h) and GPT-4o-mini degenerates to flagging nearly every window without forecasts. Key limitations: no confidence intervals or repeated runs for stochastic LLM inference, a fixed threshold despite acknowledged probability miscalibration, label thresholds derived from the full dataset including test folds, a single utility so transferability claims are untested, and unquantified cost benefits.
Review complete
Assessment at a glance
6
risk flags identified
● high 0
● medium 4
● low 2
4 strengths · 4 weaknesses · 5 author questions
AI text signal = Binoculars zero-shot detector score over 20 sampled passages (Fast-DetectGPT co-reported) — absence does not prove human authorship, and a flag does not imply misconduct.
Levels reflect verification complexity — not a quality score. Evolve never rates your paper.
Key claims under review
- Supervised ML (LightGBM, Logistic Regression) outperforms all four zero-shot GPT models on macro-F1 and precision in every horizon and weather configuration, with a gap of 0.04-0.13 macro-F1.
- Newer-generation LLMs (GPT-4.1 family) achieve competitive zero-shot macro-F1 (0.57-0.63) with no labeled outage data, and are more stable across forecast horizons than the GPT-4o generation.
- LLMs derive most of their predictive signal from current weather conditions and gain only marginally from forecast-window inputs, whereas ML models consistently benefit from forecasts.
- LLMs provide complementary operational value (natural-language rationales, no retraining for new territories or horizons), so combining them with supervised models is presented as best practice.
Strengths
First-of-kind problem framing with a clean protocol
Zero-shot LLM benchmark for weather-driven outage severity, with window-dependent thresholds (tau3=5, tau6=8, tau12=10 outages at ~5% positive rate, Section 5) each given a physical justification, and both observation-only and forecast-input modes evaluated.
Fair, symmetric evaluation design
ML and LLMs share identical Leave-One-Year-Out folds (Section 6.2), and the 0.5 binarization threshold is deliberately untuned to keep the evaluation fully zero-shot — a choice against the LLMs' own interest that strengthens the comparison's credibility.
Honest reporting of failure modes
Statistical grounding precedes modeling
Weaknesses
No uncertainty quantification for the headline comparison
Operating-point confound in LLM evaluation
Label thresholds leak test-fold information
Transferability, scalability, and cost claims are asserted, not tested
Full Review Report
$1.99 · one-timeUnlock the complete preliminary analysis Evolve produced for this paper.
Report preview
Scope & Claims
The paper (HICSS-60; arXiv:2609.04272) claims the first zero-shot application of large language models to weather-driven forced-outage risk prediction in a distribution grid. Four OpenAI models (GPT-4o, GPT-4o-mini, GPT-4.1, GPT-4.1-mini) are benchmarked against Logistic Regression and LightGBM on six years (2018-2023) of feeder-level outage records from a central Texas utility, across 3/6/12-hour horizons and two input modes (current observations vs. window-aggregated forecasts). Three claims are advanced: (1) supervised ML outperforms LLMs on macro-F1 and precision; (2) newer-generation LLMs are competitive without any labeled data; (3) LLMs add transferability, scalability, and interpretability, so hybrid deployment is best practice. Claims 1-2 are supported by the reported tables; claim 3 is an argued design case, not an experiment.
The remaining sections cover Methodology Assessment, Statistical Evidence, Results & Reproducibility, Limitations & Risks, and Verdict & Recommendations.
- All 4 strengths & 4 weaknesses, analyzed in detail
- 6 risk flags with severity, section & reviewer questions
- Methods & statistical evidence assessment
- 5 questions a peer reviewer would ask
- Full six-dimension evaluation report & recommendations
One-time purchase · this paper only
One-time purchase, this paper only. AI-generated preliminary analysis — not peer review.
Frequently asked questions
Do LLMs beat machine learning at predicting power outages?
Not in this study. On six years of data from a central Texas utility, supervised models (LightGBM and Logistic Regression) beat all four zero-shot GPT models on macro-F1 and precision in every tested configuration — for example, LightGBM reached 0.74 macro-F1 at the 3-hour horizon with weather forecasts, versus 0.61 for the best LLM, an overall gap of 0.04 to 0.13 macro-F1.
Which LLM performed best for outage risk prediction?
The newer GPT-4.1 and GPT-4.1-mini models, with macro-F1 of 0.57-0.63 and no training on outage data. GPT-4o tended to over-predict high risk (recall 0.82 but precision only 0.14 at the 12-hour horizon), and GPT-4o-mini without forecast input flagged nearly every time window as high risk (recall above 0.95, precision about 0.05).
What data did the study use?
Feeder-level outage records from a major Texas utility covering 2018-2023 (six years), combined with hourly weather reanalysis from Open-Meteo sampled on a 10x10 grid over the service area. February 2021 (Winter Storm Uri) was excluded. High-risk labels mark windows where outage counts exceed thresholds of 5, 8, or 10 outages for the 3-, 6-, and 12-hour horizons, giving roughly a 5% positive rate.
Can LLMs predict grid outages without any training data?
Partially, according to this paper. Zero-shot GPT-4.1 models reached macro-F1 of 0.57-0.63 using only weather inputs and their pre-trained knowledge, compared with 0.61-0.74 for trained models — useful, but consistently behind. The study covers a single utility, uses a fixed 0.5 decision threshold without calibration, and reports no confidence intervals, so the precision of the comparison is uncertain.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- Figures 1-3 are graphical; only their captions and in-text descriptions were available in the extracted text, so visual patterns in the time-series and resilience plots could not be independently verified.
Go deeper on this paper
Wrote a paper? Stress-test it before reviewers do.
Upload your manuscript for the same structured scrutiny — reasoning, statistics, evidence, figures, and reproducibility.