In-Context Residual Calibration for Uncertainty Quantification of Energy Time Series over Graphs
cs.LG, stat.ML
Submitted: 2026-06-30
Updated: 2026-10-07
Comments: Under-review
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
The gist: Accurate energy demand forecasting is essential for the reliable operation and planning of modern sustainable energy systems.
Terminology
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 operational planning under uncertainty. Conformal prediction provides a principled and model-agnostic framework for uncertainty quantification with statistical coverage guarantees, making it particularly attractive for safety-critical energy applications. However, existing conformal prediction approaches often fail to fully capture the complex spatial-temporal structure of energy systems. To address these limitations, we propose STOIC (Spatial-Temporal Graph Conformal Prediction with In-Context Learning), a novel framework that integrates graph-based forecasting with the zero-shot calibration capabilities of tabular foundation models. STOIC first generates point forecasts using an STGNN and subsequently reformulates spatial-temporal residuals into a tabular representation suitable for in-context learning. Leveraging a tabular foundation model, STOIC calibrates prediction intervals without task-specific retraining, effectively capturing both sequential and relational dependencies. We evaluate STOIC on five diverse benchmarks, including synthetic simulations as well as real-world electricity and district heating networks. Across all datasets, STOIC consistently outperforms existing conformal prediction baselines, delivering more reliable and robust uncertainty estimates for complex graph-structured energy time series.
Sources
- Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
- Conformal Inference for Time Series over Graphs
- MOMENT: A Family of Open Time-series Foundation Models
- TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Models
- TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
- Spatial Conformal Inference through Localized Quantile Regression
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