Predicting Power Grid Behavior with AI and Physics
![The system architecture leverages a four-layer, residual Graph Attention Network (GATv2) encoder-informed by bus-type awareness and a supervision mask-to predict voltage magnitudes [latex]V_m[/latex], phase angles δ, and both active [latex]P_g[/latex] and reactive [latex]Q_g[/latex] power generation, all within a unified decoding trunk and guided by a physics-informed loss function [latex]\mathcal{L}_{phy}[/latex] that incorporates predicted outputs and supervisory signals.](https://arxiv.org/html/2603.16879v1/x1.png)
A new framework leverages graph neural networks and physics-informed learning to accurately and adaptively model complex power system flows.
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