用天气数据预测极端天气下停电，准确率提升8.4%。
SARIMAX-Based Power Outage Prediction During Extreme Weather Events
- 结合天气与历史停电数据，通过特征筛选和时序嵌入增强模型输入。
- 24小时预测RMSE达177.2，比基线方法降低8.4%。
- 支持短期与中期预测，适合电力系统应急调度参考。
本研究构建了一个基于SARIMAX的短期停电预测系统，用于极端天气事件下的电力中断预测。利用密歇根州各县的每小时停电次数与全面的气象特征数据，设计了两阶段特征工程流程：首先清洗数据以剔除零方差和未知特征，随后通过相关性筛选去除高度相关的预测变量。选定特征经时序嵌入、多尺度滞后特征及对应滞后气象变量的扩展后，作为外生变量输入SARIMAX模型。为应对数据不规则与数值不稳定性，采用标准化处理，并实施分层拟合策略，包括顺序优化方法、收敛失败时自动降级至ARIMA，以及历史均值作为最终保障预测。模型分别针对短（24小时）与中（48小时）期预测目标进行优化，以RMSE为评估指标。结果表明，该方法在24小时预测上取得RMSE=177.2，相比基线方法（RMSE=193.4）提升8.4%，验证了特征工程与鲁棒优化策略在极端天气停电预测中的有效性。
原文摘要 · Abstract (English)
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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