SARIMAX-Based Power Outage Prediction During Extreme Weather Events

• URL: http://arxiv.org/abs/2511.01017v1
• Date: Sun, 02 Nov 2025 17:13:58 GMT
• Title: SARIMAX-Based Power Outage Prediction During Extreme Weather Events
• Authors: Haoran Ye, Qiuzhuang Sun, Yang Yang,
• Abstract summary: This study develops a SARIMAX-based prediction system for short-term power outage forecasting during extreme weather events.<n>Using hourly data from Michigan counties with outage counts and comprehensive weather features, we implement a systematic two-stage feature engineering pipeline.<n>The selected features are augmented with temporal embeddings, multi-scale lag features, and weather variables with their corresponding lags as inputs to the SARIMAX model.
• Score: 9.853802100348892
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study develops a SARIMAX-based prediction system for short-term power outage forecasting during extreme weather events. Using hourly data from Michigan counties with outage counts and comprehensive weather features, we implement a systematic two-stage feature engineering pipeline: data cleaning to remove zero-variance and unknown features, followed by correlation-based filtering to eliminate highly correlated predictors. The selected features are augmented with temporal embeddings, multi-scale lag features, and weather variables with their corresponding lags as exogenous inputs to the SARIMAX model. To address data irregularity and numerical instability, we apply standardization and implement a hierarchical fitting strategy with sequential optimization methods, automatic downgrading to ARIMA when convergence fails, and historical mean-based fallback predictions as a final safeguard. The model is optimized separately for short-term (24 hours) and medium-term (48 hours) forecast horizons using RMSE as the evaluation metric. Our approach achieves an RMSE of 177.2, representing an 8.4\% improvement over the baseline method (RMSE = 193.4), thereby validating the effectiveness of our feature engineering and robust optimization strategy for extreme weather-related outage prediction.

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