Extreme weather events, such as severe storms, hurricanes, snowstorms, and ice storms, which are exacerbated by climate change, frequently cause widespread power outages. These outages halt industrial operations, impact communities, damage critical infrastructure, profoundly disrupt economies, and have far-reaching effects across various sectors. To mitigate these effects, the University of Connecticut and Eversource Energy Center have developed an outage prediction modeling (OPM) system to provide pre-emptive forecasts for electric distribution networks before such weather events occur. However, existing predictive models in the system do not incorporate the spatial effect of extreme weather events. To this end, we develop Spatially Aware Hybrid Graph Neural Networks (SA-HGNN) with contrastive learning to enhance the OPM predictions for extreme weather-induced power outages. Specifically, we first encode spatial relationships of both static features (e.g., land cover, infrastructure) and event-specific dynamic features (e.g., wind speed, precipitation) via Spatially Aware Hybrid Graph Neural Networks (SA-HGNN). Next, we leverage contrastive learning to handle the imbalance problem associated with different types of extreme weather events and generate location-specific embeddings by minimizing intra-event distances between similar locations while maximizing inter-event distances across all locations. Thorough empirical studies in four utility service territories, i.e., Connecticut, Western Massachusetts, Eastern Massachusetts, and New Hampshire, demonstrate that SA-HGNN can achieve state-of-the-art performance for power outage prediction.

翻译：极端天气事件，如因气候变化加剧的强风暴、飓风、暴风雪和冰暴，经常导致大范围电力中断。这些中断阻碍工业运营、影响社区、破坏关键基础设施，深刻扰乱经济，并在各个领域产生深远影响。为减轻这些影响，康涅狄格大学和艾维索斯能源中心开发了一款停电预测建模（OPM）系统，以便在此类天气事件发生前，为配电网络提供预判性预测。然而，系统中现有的预测模型并未纳入极端天气事件的空间效应。为此，我们开发了基于空间感知混合图神经网络（SA-HGNN）的对比学习方法，以增强OPM对极端天气导致停电的预测能力。具体而言，我们首先通过空间感知混合图神经网络（SA-HGNN）对静态特征（如土地覆盖、基础设施）和事件特定动态特征（如风速、降水量）的空间关系进行编码。接着，我们利用对比学习来处理与不同类型极端天气事件相关的不平衡问题，并通过最小化相似位置之间的事件内距离及最大化所有位置间的事件间距离，生成位置特定的嵌入。在四个公用事业服务区域（即康涅狄格州、马萨诸塞州西部、马萨诸塞州东部和新罕布什尔州）进行的全面实证研究表明，SA-HGNN在停电预测中能够实现最先进的性能。