AI RESEARCH
GRAFT: Grid-Aware Load Forecasting with Multi-Source Textual Alignment and Fusion
arXiv CS.LG
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ArXi:2512.14400v2 Announce Type: replace Electric load is simultaneously affected across multiple time scales by exogenous factors such as weather and calendar rhythms, sudden events, and policies. Therefore, this paper proposes GRAFT (GRid-Aware Forecasting with Text), which modifies and improves STanHOP to better grid-aware forecasting and multi-source textual interventions. Specifically, GRAFT strictly aligns daily-aggregated news, social media, and policy texts with half-hour load, and realizes text-guided fusion to specific time positions via cross-attention during both.