OutageDiT：面向停电预测与场景生成的基础模型
OutageDiT: A Foundation Model for Power Outage Forecasting and Scenario Generation
AI 导读
研究者推出 OutageDiT，一个基于全美停电与天气记录训练的基础模型，能以 15 分钟分辨率生成七天停电轨迹。该模型用条件编码器处理历史与未来协变量，浅层 flow-matching 解码器采样完整轨迹，在单一深度生成模型中实现点预测、不确定性量化和条件场景生成。在停电预测基准上，OutageDiT 的预测精度和场景质量优于强基线，并支持对留出区域的零样本迁移。
正文 · 原文
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 15-minute resolution, trained on outage and weather records across the United States. Specifically, a condition encoder processes historical context and known future covariates once per forecast, and a shallow flow-matching decoder reuses the resulting horizon-aligned states to sample complete outage trajectories. This enables point forecasting, uncertainty quantification, and conditional scenario generation 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 scenario generation as a bridge from outage forecasting to operational planning under uncertainty.
| Comments: | 11 pages, 3 figures, 3 tables. Accepted at the NeurIPS 2026 Second Workshop on MLxOR |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.01896 [cs.LG] |
| (or arXiv:2609.01896v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.01896 arXiv-issued DOI via DataCite |
Submission history
From: Yunqin Zhu [view email]
[v1]
Tue, 1 Sep 2026 21:54:17 UTC (172 KB)
[v2]
Tue, 27 Oct 2026 21:59:08 UTC (357 KB)
来源：arXiv cs.AI · arxiv.org