Structure-Grounded Test-Time Adaptation of Tabular Foundation Models for Power Outage Prediction

Weather-induced power outages disrupt critical infrastructure, making accurate forecasting essential for proactive response. Existing methods are typically trained once on heterogeneous historical weather events, limiting their ability to adapt to rare high-impact events. Tabular foundation models such as TabPFN instead leverage in-context labeled examples at inference time, enabling event-specific prediction without retraining. However, their native inference does not explicitly capture event-specific spatial dependencies among neighboring locations, which may vary substantially across events, particularly during extreme events. To resolve this limitation, we propose Structure-Grounded Test-Time Adaptation (SG-TTA), which uses spatial structure as test-time feedback to adapt a lightweight module while keeping the tabular in-context learner frozen. For each incoming event, SG-TTA retrieves relevant locations in historical events and their neighborhoods to augment in-context support and provide cross-event spatial references for alignment, while maintaining within-event consistency via neighbor reconstruction. Experiments across four real-world service territories show that SG-TTA consistently improves both TabPFN and TabICL, with the TabPFN variant achieving the lowest MAE in every territory. Further analysis in Connecticut (CT) confirms performance gains across outage-severity levels, especially on high-impact events.