用图模型预测停电数量范围，提升电网灾后恢复能力。
Spatio-Temporal Conformal Prediction for Power Outage Data
- 基于图结构的时序分位数预测，生成停电数置信区间。
- 在多个极端天气受灾州的季度数据上验证，覆盖未来时段预测。
- 适合电力系统应急规划与韧性建设人员参考。
近年来，日益不可预测且严重的全球气候模式频繁引发长时间停电。提升电网应对、适应并从重大中断中恢复的能力——即韧性——已成为电力行业关键需求。为实现快速恢复，准确预测未来停电数量至关重要。我们不依赖简单点估计，而是分析大量15分钟粒度的停电数据，提出一种图结构的分位数预测方法，为多个州在未来一段时间内的停电数量生成精确的预测区间。通过在多个受极端天气事件影响导致大规模停电的州进行广泛数值实验，验证了该方法的有效性。
原文摘要 · Abstract (English)
In recent years, increasingly unpredictable and severe global weather patterns have frequently caused long-lasting power outages. Building resilience, the ability to withstand, adapt to, and recover from major disruptions, has become crucial for the power industry. To enable rapid recovery, accurately predicting future outage numbers is essential. Rather than relying on simple point estimates, we analyze extensive quarter-hourly outage data and develop a graph conformal prediction method that delivers accurate prediction regions for outage numbers across the states for a time period. We demonstrate the effectiveness of this method through extensive numerical experiments in several states affected by extreme weather events that led to widespread outages.
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