Thunderstorm-driven power outages are difficult to predict because most storms do not cause damage, convective processes occur rapidly and chaotically, and the available public data are noisy and incomplete. Severe convective storms now account for a large and rising share of U.S. weather losses, yet thunderstorm-induced outages remain understudied. We develop a 48-hour early-warning model for summer thunderstorm-related outages in Michigan using only open-source outage (EAGLE-I) and weather (METAR) data. Relative to prior work, we (i) rely solely on public data, (ii) preserve convective extremes from a sparse station network via parameter-specific kriging and causal spatiotemporal features, and (iii) use a multi-level LSTM-based architecture evaluated on event-centric peak metrics. The pipeline builds rolling and k-NN inverse-distance aggregates to capture moisture advection, wind shifts, and pressure drops. A two-stage design uses a logistic gate followed by a long short-term memory (LSTM) regressor to filter routine periods and limit noise exposure. Evaluation focuses on state-level peaks of at least 50,000 customers without power, using hits, misses, false alarms, and peak-conditional MASE (cMASE) within 48-hour windows, with uncertainty quantified by block bootstrapping. On the test sample, the Two-Stage model detects more peaks with only one additional false alarm and reduces cMASE near peaks, providing event-focused early warnings without the utility-specific data.

翻译：雷暴驱动型停电难以预测，主要原因在于多数风暴并不引发损害、对流过程发生迅速且混沌，以及可用的公共数据存在噪声且不完整。当前强对流风暴在美国天气相关损失中占比巨大且持续上升，然而雷暴引发的停电问题仍未得到充分研究。本研究仅利用开源停电数据（EAGLE-I）与气象数据（METAR），开发了针对密歇根州夏季雷暴相关停电的48小时预警模型。相较于先前研究，本工作具有以下特点：（i）完全依赖公开数据；（ii）通过参数特异性克里金插值与因果时空特征，在稀疏站点网络中保留对流极端值；（iii）采用基于多层级LSTM的架构，并以事件为中心的峰值指标进行评估。该流程构建了滚动聚合与k近邻反距离加权聚合，以捕捉水汽平流、风向转变及气压骤降。模型采用两阶段设计：先通过逻辑门控筛选常规时段，再经由长短期记忆（LSTM）回归器进行预测，从而过滤噪声干扰。评估聚焦于州级尺度至少50,000用户停电的峰值事件，采用命中、漏报、误报及峰值条件平均绝对标度误差（cMASE）等指标，在48小时时间窗内通过区块自助法量化不确定性。在测试样本中，两阶段模型能检测到更多峰值事件且仅增加一次误报，同时降低了峰值附近的cMASE值，实现了无需电力公司专有数据、以事件为核心的早期预警。