用生成模型预测停电轨迹，支持多场景模拟和零样本迁移。
OutageDiT: A Generative Foundation Model for Power Outage Forecasting and Scenario Simulation
- 基于历史停电与天气数据，构建七天高分辨率停电生成模型。
- 在多个基准上提升预测精度，生成样本更贴近真实极端事件。
- 适合电力系统规划者用于不确定性下的应急决策模拟。
停电规划需在事件发生前生成可能情景，这些情景需反映停电规模、时间与持续时间的不确定性，同时保持时间依赖性。然而，极端事件罕见，单一区域数据中极端停电与恢复模式样本极少。为此，我们提出 OutageDiT，一种面向美国全境停电与天气记录的生成基础模型，可生成七天、每15分钟分辨率的停电轨迹。具体地，条件编码器一次性处理历史背景与未来协变量，浅层流解码器复用对齐时序状态生成完整轨迹。生成样本支持点预测、不确定性量化及条件事件模拟，仅需一个深度生成模型。在多个停电预测基准上，OutageDiT优于强基线模型，且实现对未见区域的零样本迁移。结果表明，条件停电模拟可作为从预测到不确定环境下运营规划的桥梁。
原文摘要 · Abstract (English)
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.
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