Relational and Sequential Conformal Inference for Energy Time Series over Graphs via Foundation Models
arXiv stat.ML1mo4 min read
arXiv:2606.31804v1 Announce Type: cross Abstract: Accurate energy demand forecasting is essential for the reliable operation and planning of modern sustainable energy systems. Spatial-temporal graph neural networks (STGNNs) have recently achieved strong performance in point forecasting by jointly modeling temporal dynamics and relational dependencies across interconnected energy nodes. However, in real-world energy systems, accurate point forecasts alone are insufficient, as operators also require reliable uncertainty estimates to support risk-aware decision-making, grid stability, and operati
