Data Leakage Inflates Generalizability of Power Outage Prediction Models
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
EvoBench is a diagnostic benchmark framework designed to reveal where and when machine‑learning models for environmental variables fail, rather than relying on a single average accuracy score. It tracks model performance over time and across spatial extrapolation, highlighting issues such as non‑stationarity, poor performance at unmonitored sites, and under‑prediction of rare events like floods. In a case study on daily streamflow prediction across 9,000 U.S. basins, EvoBench shows that no model dominates all scenarios and that targeted fixes can mitigate identified weaknesses.
OutageDiT is a foundation model designed to generate seven‑day power‑outage trajectories at 15‑minute resolution. It uses a condition encoder to process historical context and future covariates, and a shallow flow‑matching decoder to sample complete outage trajectories, enabling point forecasting, uncertainty quantification, and conditional scenario generation. The model improves forecast accuracy and scenario quality over strong baselines and can transfer zero‑shot to held‑out regions, linking outage forecasting to operational planning under uncertainty.
The study evaluates how forecast lead time influences the effectiveness of outage predictions for crew deployment using AI weather models GraphCast and FourCastNet. It finds that the ability to capture outage burden decreases with longer lead times, with a notable drop between three and five days. Early deployment with all requested crews outperforms late deployment with fewer crews, and the study also examines the impact of antecedent land surface predictors and calibration of prediction intervals for tropical cyclone events.
OutageDiT is a generative foundation model that produces seven‑day power‑outage trajectories at quarter‑hour resolution, trained on nationwide outage and weather data. It uses a condition encoder to process historical context and future covariates, and a shallow flow decoder to generate full trajectories, enabling point forecasting, uncertainty quantification, and conditional event simulation. The model outperforms strong baselines on forecasting benchmarks and can transfer zero‑shot to unseen regions, linking outage simulation to operational planning under uncertainty.
arXiv:2607. 16050v1 Announce Type: new Abstract: Pluvial (rainfall-driven) flooding accounts for 45% of National Flood Insurance Program (NFIP) claims in the United States and is harder to predict than its riverine and coastal counterparts, with existing approaches limited to coarse resolution, regional domains, or computationally intensive process-based models unsuitable for daily continental-scale use.
The paper presents a modular, scalable framework for estimating the probability of failure (PoF) of power‑line assets using geospatial machine learning. It integrates diverse environmental predictors—topography, vegetation indices, lightning climatology, proximity features, and operational records—to model vegetation‑ and lightning‑related failure modes. The architecture is designed to be computationally efficient, easily extensible to new data sources, and suitable for large‑scale utility deployment, enabling asset‑level risk stratification for inspection and resilience planning.