The increasing complexity and scale of smart grids necessitate efficient and accurate real-time anomaly detection mechanisms to ensure grid reliability and security. Traditional detection methods often fall short in capturing the complex spatial and temporal dependencies inherent in smart grid data. This paper proposes a novel approach leveraging Graph Neural Networks (GNNs) to detect anomalies in smart grids by modeling the grid as a graph where nodes represent measurement points and edges represent electrical or communication connections. Our method exploits the graph structure and temporal dynamics to identify anomalies such as faults and cyber-attacks with high accuracy and low latency. Experimental evaluations on real and synthetic datasets demonstrate that the proposed GNN-based framework outperforms conventional machine learning models, offering a scalable and effective solution for real-time anomaly detection in smart grids.
Muhammad Al-Azar· International Journal of Art...· 0 citations
The increasing presence of heterogeneous data sources in modern information systems has intensified the need for intelligent data integration processes capable of handling semantic complexity, structural diversity, and dynamic changes. Traditional data integration methods, primarily based on relational schemas and syntactic mappings, struggle to address semantic heterogeneity in large-scale distributed environments. Knowledge graphs have emerged as a powerful paradigm, enabling semantically rich, flexible, and scalable integration by representing data as interconnected entities with metadata, ontologies, and inference capabilities. Using technologies such as RDF and OWL, knowledge graphs support interoperability, contextual reasoning, and unified data views across systems. This paper examines knowledge graph-based intelligent data integration systems, focusing on their architecture, methodology, and practical applications. It highlights their advantages in schema alignment, entity resolution, and semantic enrichment over traditional ETL approaches. The integration of machine learning techniques further enhances automation in data mapping, anomaly detection, and knowledge discovery. A systematic framework is proposed, covering ontology design, data ingestion, graph construction, and query optimization. A conceptual case study demonstrates improved integration accuracy, scalability, and query performance. Evaluation results indicate enhanced data quality, interoperability, and reasoning capabilities, along with reduced integration latency. Overall, knowledge graphs serve as a key enabler for next-generation intelligent data integration, supporting complex relationships and data-driven decision-making. Future work includes improving scalability, real-time processing, and integration with deep learning models.
Muhammad Al-Azar· International Journal of App...· 0 citations
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