Early-Warning of Thunderstorm-Driven Power Outages with a Two-Stage Machine Learning Model

Thunderstorm-driven outages are difficult to predict because most storms do not cause damage, convective processes occur rapidly and chaotically, and the available public data are both noisy and incomplete. We develop a 24-48 h early-warning model for summer, thunderstorm-related outages in Michigan using only open sources (EAGLE-I for ground truth; METAR for weather). We use the publicly released EAGLE-I outage dataset (2014-2022), maintained by Oak Ridge National Laboratory for the U.S. Department of Energy. The pipeline preserves convective micro-signals from a sparse station network via parameter-specific kriging with hourly variograms and targeted overdrafting to retain extremes, and builds causal spatio-temporal features (lags/rolling statistics; k-NN/IDW spatial aggregates) capturing precursors of severe convection (moisture advection, wind shifts, and pressure drops). The two-stage model design, combining a logistic gate and an LSTM regressor, limits routine periods and reduces noise exposure. The study uses event-centric metrics (cluster-based hits/misses/false alarms) and peak-conditional MASE (cMASE) in +/-Delta-hour windows around state-level peaks (>= 50,000), with uncertainty quantified by hourly moving-block bootstrap. On the test sample, Two-Stage detects more reference peaks across all windows (e.g., at +/-48 h it records 3/4 vs. 2/4; F1 66.7% vs. 57.1%) with one extra false alarm. Near peaks, it shows modest amplitude gains (2-3% lower cMASE at +/-0-12 h; bootstrap medians +9-13% at +/-6-12 h) but small losses at +/-36-48 h (~3-4%). Overall, errors are comparable to the one-step LSTM baseline. SHAP analysis confirms moisture-advection and wind/gust precursors, underscoring the value of the feature engineering. Despite open-data noise, the feature-driven pipeline yields actionable, event-focused early warnings for thunderstorm outages.