停电预测模型正日益用于气候驱动的基础设施风险评估，但当前的评估方法掩盖了这些模型是否能泛化至此类应用所需的新型场景。我们识别出停电预测模型中三种常见的方法学选择，它们显著影响模型在空间、时间和事件维度上的泛化能力。我们利用2018–2023年美国东海岸公开数据、基于气象再分析与土地覆被数据构建的特征集，以及GeoAI基础模型（Prithvi WxC）生成的嵌入表示，对比不同方法学决策对预测性能的影响。具体而言，我们采用多种测试集选取策略评估模型性能，包括无过滤的随机划分、留一州法（leave-one-state-out）和留一事件法（leave-one-event-out），其设计逐步逼近真实部署条件。尽管随机训练-测试划分可获得优异性能，但我们证明该结果因空间与时间自相关性而被高估。在空间与时间留出实验中，预测准确性显著下降，模型常无法优于简单的零假设基线。引入GeoAI基础模型嵌入仅带来有限且不一致的提升，主要体现在空间泛化方面，但未能改善事件层面的迁移能力。上述发现表明，在当前数据可用性与评估实践下，公开训练的停电预测模型所提供的运行价值有限且不确定。未来进展可能需依赖更全面的数据覆盖、更贴近实际的评估协议，以及研究重心从边际建模改进转向解决结构性数据约束。

Power outage prediction models are increasingly used in assessments of climate-driven infrastructure risk, yet current evaluation practices obscure whether these models generalize to the novel conditions such applications require. We identify three common methodological choices in power outage prediction models that influence their ability to generalize across spatial, temporal, and event-based settings. We compare the predictive performance impacts of different methodological decisions using publicly available data for the U.S. East Coast from 2018 to 2023 and feature sets derived from weather reanalysis and land-cover data, and embeddings from a GeoAI foundation model (Prithvi WxC). Specifically, we assess model performance under multiple test selection strategies, including unfiltered random splits, leave-one-state-out, and leave-one-event-out designs, which increasingly approximate real-world deployment conditions. While random train-test splits yield strong performance, we show that these results are inflated by spatial and temporal autocorrelation. Under spatial and temporal holdout experiments, predictive accuracy degrades substantially, with models often failing to outperform a simple null baseline. Incorporating GeoAI foundation model embeddings yields limited and inconsistent improvements, primarily for spatial generalization, and does not resolve poor event-level transferability. These findings suggest that, given current data availability and evaluation practices, publicly trained outage prediction models offer limited and uncertain operational value. Progress will likely require improved data coverage, more realistic evaluation protocols, and a shift in focus from marginal modeling advances toward addressing structural data constraints.