OutageDiT: A Generative Foundation Model for Power Outage Forecasting and Scenario Simulation

2026-09-01

AI Summary

为解决电力中断预测数据稀缺问题，提出OutageDiT模型，通过历史和未来气象数据生成高分辨率的中断情景，提高预测精度。

Abstract

Power-outage planning requires scenarios before an event occurs. These scenarios must represent uncertainty in magnitude, timing, and duration while preserving temporal dependence. However, severe events are rare, and data from any single region contain few examples of extreme outage and restoration patterns. To address this challenge, we introduce OutageDiT, a foundation model for generating seven-day outage trajectories at quarter-hour resolution, trained on outage and weather records across the United States. Specifically, a condition encoder processes the historical context and known future covariates once per forecast, and a shallow flow decoder reuses the resulting horizon-aligned states to generate complete trajectories. The resulting samples support point forecasting, uncertainty quantification, and conditional event simulation within one deep generative model. Across outage forecasting benchmarks, OutageDiT improves forecast accuracy and scenario quality over strong baselines and supports zero-shot transfer to held-out regions. Together, these results position conditional outage simulation as a bridge from outage forecasting to operational planning under uncertainty.

Problem

• Power Outage
• Scenario Simulation
• Uncertainty
• Temporal Dependence
• Rare Events

Innovation

• foundation model
• power outage forecasting
• scenario simulation
• conditional encoder
• shallow flow decoder