Relational and Sequential Conformal Inference for Energy Time Series over Graphs via Foundation Models
arXiv:2606.31804v1 Announce Type: new 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 operation
The increasing integration of renewable energy sources and the growing complexity of energy grids necessitate more sophisticated and reliable forecasting methods to ensure stability.
Reliable energy forecasting with uncertainty quantification is critical for grid operators to manage demand, prevent outages, and integrate intermittent renewables efficiently, directly impacting energy stability and economic activity.
This advancement moves beyond simple point forecasts to provide probabilistic estimates, allowing for more robust and risk-aware decision-making in real-time energy system operations.
- · Energy grid operators
- · Renewable energy companies
- · AI/ML developers
- · Smart city initiatives
- · Traditional forecasting models (deterministic)
- · Energy systems with poor data infrastructure
Improved grid stability and reduced energy waste through more accurate demand response.
Accelerated adoption of diverse renewable energy sources due to enhanced grid management capabilities.
Potential for new financial instruments and markets based on more granular and reliable energy uncertainty data.
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Read at arXiv cs.LG