用图注意力网络预测飓风引发的大规模停电持续时间
Gated Graph Attention Networks for Predicting Duration of Large Scale Power Outages Induced by Natural Disasters
- 结合图注意力与门控循环单元，捕捉停电的空间依赖性
- 在六场飓风数据上表现优于基准模型，提升预测精度
- 适合能源系统韧性研究者和灾害应急规划人员
气候变化背景下，由自然灾害引发的大规模停电事件频发且持续时间长，造成重大经济损失和社会影响。准确预测停电持续时间对提升能源基础设施韧性至关重要。本文将该问题建模为机器学习任务，针对数据中的高阶空间依赖、大规模停电事件数量有限、事件类型异质性以及区域影响差异等现实挑战，提出双模态门控图注意力网络（BiGGAT）。该模型融合图注意力网络（GAT）与门控循环单元（GRU），有效捕捉复杂空间特征。在东南部六场主要飓风的停电数据上进行归纳学习评估，实验结果表明，BiGGAT显著优于基准模型，展现出更强的预测能力。
原文摘要 · Abstract (English)
The occurrence of large-scale power outages induced by natural disasters has been on the rise in a changing climate. Such power outages often last extended durations, causing substantial financial losses and socioeconomic impacts to customers. Accurate estimation of outage duration is thus critical for enhancing the resilience of energy infrastructure under severe weather. We formulate such a task as a machine learning (ML) problem with focus on unique real-world challenges: high-order spatial dependency in the data, a moderate number of large-scale outage events, heterogeneous types of such events, and different impacts in a region within each event. To address these challenges, we develop a Bimodal Gated Graph Attention Network (BiGGAT), a graph-based neural network model, that integrates a Graph Attention Network (GAT) with a Gated Recurrent Unit (GRU) to capture the complex spatial characteristics. We evaluate the approach in a setting of inductive learning, using large-scale power outage data from six major hurricanes in the Southeastern United States. Experimental results demonstrate that BiGGAT achieves a superior performance compared to benchmark models.
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