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Towards Intelligent Energy Security: A Unified Spatio-Temporal and Graph Learning Framework for Scalable Electricity Theft Detection in Smart Grids
arXiv – CS AI|AbdulQoyum A. Olowookere, Usman A. Oguntola, Ebenezer. Leke Odekanle, Maridiyah A. Madehin, Aisha A. Adesope|
🤖AI Summary
Researchers have developed SmartGuard Energy Intelligence System (SGEIS), an AI framework that combines machine learning, deep learning, and graph neural networks to detect electricity theft in smart grids. The system achieved 96% accuracy in identifying high-risk nodes and demonstrates strong performance with practical applications for energy security.
Key Takeaways
- →SGEIS integrates multiple AI technologies including LSTM, TCN, Autoencoders, and Graph Neural Networks for comprehensive electricity theft detection.
- →The system achieved over 96% accuracy in identifying high-risk nodes and Gradient Boosting attained 0.894 ROC-AUC performance.
- →The framework combines temporal, spatial, and statistical analysis to improve detection robustness in smart grid environments.
- →Non-Intrusive Load Monitoring (NILM) enhances system interpretability by disaggregating appliance-level consumption patterns.
- →The solution addresses significant economic losses from electricity theft while providing scalable deployment potential for real-world smart grids.
#artificial-intelligence#smart-grids#machine-learning#energy-security#graph-neural-networks#deep-learning#electricity-theft#sgeis#temporal-analysis#ensemble-learning
Read Original →via arXiv – CS AI
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