Real-Time Anomaly Detection in Smart Grid Networks Using Deep Learning with Cross-Domain Generalization and Multi-Task Learning

Real-time anomaly detection in smart grid networks is critical for ensuring the reliability and security of energy distribution systems. Traditional methods often struggle with the complexity and volume of data generated by these networks. This paper presents a novel deep learning-based approach that integrates Cross-Domain Generalization (CDG) and Multi-Task Learning (MTL) to enhance the detection of anomalies in smart grid data. By leveraging diverse datasets and iterative learning techniques, our method improves model robustness and generalization. Experimental results demonstrate significant improvements over baseline methods, showcasing the effectiveness of our approach. We provide comprehensive evaluations and discuss the broader implications for anomaly detection in industrial applications.