Academic

Towards Intelligent Energy Security: A Unified Spatio-Temporal and Graph Learning Framework for Scalable Electricity Theft Detection in Smart Grids

arXiv:2604.03344v1 Announce Type: new Abstract: Electricity theft and non-technical losses (NTLs) remain critical challenges in modern smart grids, causing significant economic losses and compromising grid reliability. This study introduces the SmartGuard Energy Intelligence System (SGEIS), an integrated artificial intelligence framework for electricity theft detection and intelligent energy monitoring. The proposed system combines supervised machine learning, deep learning-based time-series modeling, Non-Intrusive Load Monitoring (NILM), and graph-based learning to capture both temporal and spatial consumption patterns. A comprehensive data processing pipeline is developed, incorporating feature engineering, multi-scale temporal analysis, and rule-based anomaly labeling. Deep learning models, including Long Short-Term Memory (LSTM), Temporal Convolutional Networks (TCN), and Autoencoders, are employed to detect abnormal usage patterns. In parallel, ensemble learning methods such as R

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AbdulQoyum A. Olowookere, Usman A. Oguntola, Ebenezer. Leke Odekanle, Maridiyah A. Madehin, Aisha A. Adesope · · 1 min read · 4 views
#cs.LG #cs.AI

arXiv:2604.03344v1 Announce Type: new Abstract: Electricity theft and non-technical losses (NTLs) remain critical challenges in modern smart grids, causing significant economic losses and compromising grid reliability. This study introduces the SmartGuard Energy Intelligence System (SGEIS), an integrated artificial intelligence framework for electricity theft detection and intelligent energy monitoring. The proposed system combines supervised machine learning, deep learning-based time-series modeling, Non-Intrusive Load Monitoring (NILM), and graph-based learning to capture both temporal and spatial consumption patterns. A comprehensive data processing pipeline is developed, incorporating feature engineering, multi-scale temporal analysis, and rule-based anomaly labeling. Deep learning models, including Long Short-Term Memory (LSTM), Temporal Convolutional Networks (TCN), and Autoencoders, are employed to detect abnormal usage patterns. In parallel, ensemble learning methods such as Random Forest, Gradient Boosting, XGBoost, and LightGBM are utilized for classification. To model grid topology and spatial dependencies, Graph Neural Networks (GNNs) are applied to identify correlated anomalies across interconnected nodes. The NILM module enhances interpretability by disaggregating appliance-level consumption from aggregate signals. Experimental results demonstrate strong performance, with Gradient Boosting achieving a ROC-AUC of 0.894, while graph-based models attain over 96% accuracy in identifying high-risk nodes. The hybrid framework improves detection robustness by integrating temporal, statistical, and spatial intelligence. Overall, SGEIS provides a scalable and practical solution for electricity theft detection, offering high accuracy, improved interpretability, and strong potential for real-world smart grid deployment.

Executive Summary

This article presents the SmartGuard Energy Intelligence System (SGEIS), an integrated artificial intelligence framework for electricity theft detection and intelligent energy monitoring in smart grids. The proposed system leverages supervised machine learning, deep learning, Non-Intrusive Load Monitoring, and graph-based learning to detect anomalies in both temporal and spatial consumption patterns. The authors demonstrate strong performance of their framework using a range of machine learning and deep learning models, including Gradient Boosting and Graph Neural Networks. The SGEIS framework offers a scalable and practical solution for electricity theft detection, with potential for real-world smart grid deployment. The authors' comprehensive data processing pipeline and ensemble learning methods enhance interpretability and detection robustness. Overall, this study contributes significantly to the field of smart grid security and energy intelligence.

Key Points

  • ▸ The proposed SmartGuard Energy Intelligence System (SGEIS) is an integrated artificial intelligence framework for electricity theft detection and intelligent energy monitoring in smart grids.
  • ▸ The framework combines supervised machine learning, deep learning, Non-Intrusive Load Monitoring, and graph-based learning to detect anomalies in both temporal and spatial consumption patterns.
  • ▸ The authors demonstrate strong performance of their framework using a range of machine learning and deep learning models, including Gradient Boosting and Graph Neural Networks.

Merits

Strengths in Multimodal Learning

The SGEIS framework's integration of multiple learning paradigms, including supervised machine learning, deep learning, and graph-based learning, enables a comprehensive understanding of electricity consumption patterns and robust anomaly detection.

Demerits

Data Quality and Availability

The performance of the SGEIS framework is heavily dependent on the quality and availability of the dataset, which may not be a realistic assumption in real-world smart grid scenarios where data may be incomplete, noisy, or biased.

Expert Commentary

The SGEIS framework presented in this study represents a significant advancement in the field of smart grid security and energy intelligence. The authors' innovative combination of machine learning, deep learning, and graph-based learning paradigms enables a comprehensive understanding of electricity consumption patterns and robust anomaly detection. However, the performance of the framework is heavily dependent on the quality and availability of the dataset, which may not be a realistic assumption in real-world smart grid scenarios. Nevertheless, the study's findings have important practical and policy implications, underscoring the need for more robust and intelligent energy management systems.

Recommendations

  • ✓ Future studies should investigate the scalability and adaptability of the SGEIS framework in real-world smart grid scenarios, including the impact of data quality and availability on its performance.
  • ✓ Researchers should explore the application of the SGEIS framework in other energy management areas, such as demand response and energy forecasting.

Sources

Original: arXiv - cs.LG

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