A Wide-and-Deep-Based Time Sequence Model for Predicting Power Outages Caused by Extreme Winter Storms

Abstract. In February 2021, Winter Storm Uri caused widespread power outages across Texas, affecting over 5 million people and resulting in an estimated 190 billion in damages. To support extreme weather outage resilience, this study introduces the Wide-and-Deep-Based Time Sequence Algorithm (WDTSA) for predicting power outage severity. The model combines a deep bidirectional LSTM for time-lagged weather and outage history with a wide pathway for weakly temporal features, enabling synergistic integration of heterogeneous inputs. This dual pathway design significantly outperforms standard baselines, achieving 0. 99 accuracy at coarse resolution (K=3) and 0. 84 at fine granularity (K=15), utilizing fewer parameters than expanded LSTM alternatives. Ablation and comparative analyses confirm that the performance gains arise from specialized feature routing and non-additive synergy between input groups, growingly so under complex classification tasks. County-level visualizations during Winter Storm Uri are provided to illustrate the model’s ability to anticipate outage progression, offering actionable forecasts for emergency planning. While current validation focuses on extreme events and does not offer spatial dependency modeling, the framework provides a compact and flexible foundation for resilient grid operations and targeted response in potential weather-induced disruptions.