A hybrid ML framework using temporal deep learning, ensemble methods, and GNNs reports up to 0.894 ROC-AUC and over 96% accuracy for identifying high-risk nodes in electricity theft detection.
FedDetect: A Novel Privacy-Preserving Federated Learning Framework for Energy Theft Detection in Smart Grid
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Towards Intelligent Energy Security: A Unified Spatio-Temporal and Graph Learning Framework for Scalable Electricity Theft Detection in Smart Grids
A hybrid ML framework using temporal deep learning, ensemble methods, and GNNs reports up to 0.894 ROC-AUC and over 96% accuracy for identifying high-risk nodes in electricity theft detection.