GRAFT improves electric load forecasting accuracy by aligning multi-source daily texts with half-hour load series and using cross-attention fusion, outperforming baselines on a new Australian benchmark across hourly to monthly horizons.
Probabilistic electric load forecasting: A tutorial review
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KUP-BI distills continuation-style knowledge from a train-only historical library to supply an approximate post-target proxy that is fused into forecasting backbones for improved performance on public datasets.
A hybrid deep learning model with physics regularization and SHAP analysis achieves 1.18% MAPE on ERCOT load data and up to 40.5% better performance on extreme events than its individual branches.
citing papers explorer
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GRAFT: Grid-Aware Load Forecasting with Multi-Source Textual Alignment and Fusion
GRAFT improves electric load forecasting accuracy by aligning multi-source daily texts with half-hour load series and using cross-attention fusion, outperforming baselines on a new Australian benchmark across hourly to monthly horizons.
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Beyond Extrapolation: Knowledge Utilization Paradigm with Bidirectional Inspiration for Time Series Forecasting
KUP-BI distills continuation-style knowledge from a train-only historical library to supply an approximate post-target proxy that is fused into forecasting backbones for improved performance on public datasets.
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Interpretable Physics-Informed Load Forecasting for U.S. Grid Resilience: SHAP-Guided Ensemble Validation in Hybrid Deep Learning Under Extreme Weather
A hybrid deep learning model with physics regularization and SHAP analysis achieves 1.18% MAPE on ERCOT load data and up to 40.5% better performance on extreme events than its individual branches.