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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 dependenci
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