In-Context Residual Calibration for Uncertainty Quantification of Energy Time Series over Graphs
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 operational planning under uncertainty. Conformal prediction provides a principled and model-agnostic framework for uncertainty quantification under excha...
| Authors | Keivan Faghih Niresi, Alice Cicirello, Olga Fink |
|---|---|
| Primary category | cs.LG |
| Published | 30 June 2026 |
| Last updated at source | 7 October 2026 |
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