arXiv Machine Learning
Data Leakage Inflates Generalizability of Power Outage Prediction Models
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OutageDiT: A Generative Foundation Model for Power Outage Forecasting and Scenario Simulation
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.
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Scalable Geospatial Machine Learning for Power-Line Asset Risk: Integrating Remote Sensing for Lightning and Vegetation Risk Modelling
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Evaluating Large Language Models for Forced Outage Risk Prediction: Benefits and Comparison to Machine Learning
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arXiv Machine Learning
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Decision-Oriented Uncertainty Quantification for Risk Control in Earth System Spatiotemporal Foundation Models
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