Towards Intelligent Energy Security: A Unified Spatio-Temporal and Graph Learning Framework for Scalable Electricity Theft Detection in Smart Grids
Summary: arXiv:2604.03344v1 Announce Type: cross
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.
Introduction
The increasing integration of advanced technologies in smart grids has brought about many benefits, but it has also introduced vulnerabilities, particularly in the area of electricity theft. This phenomenon not only results in economic losses but also threatens the overall reliability of energy distribution. The SmartGuard Energy Intelligence System (SGEIS) aims to address these challenges by leveraging sophisticated AI techniques to enhance electricity theft detection capabilities.
Framework Overview
The proposed system combines several advanced methodologies:
- Supervised Machine Learning: Techniques are employed for classification tasks.
- Deep Learning-Based Time-Series Modeling: Models like Long Short-Term Memory (LSTM) and Temporal Convolutional Networks (TCN) analyze temporal patterns.
- Non-Intrusive Load Monitoring (NILM): This module disaggregates appliance-level consumption from aggregate signals to enhance interpretability.
- Graph-Based Learning: Graph Neural Networks (GNNs) are utilized to model grid topology and identify correlated anomalies across interconnected nodes.
Data Processing Pipeline
A comprehensive data processing pipeline is developed, which incorporates:
- Feature Engineering: Extracting relevant features from raw data to improve model performance.
- Multi-Scale Temporal Analysis: Examining data across various time scales to capture different usage patterns.
- Rule-Based Anomaly Labeling: Establishing criteria for identifying unusual consumption patterns.
Modeling Techniques
To enhance the detection of abnormal usage patterns, deep learning models such as Autoencoders are also employed. In addition, ensemble learning methods including Random Forest, Gradient Boosting, XGBoost, and LightGBM are utilized for classification tasks. These techniques collectively improve the detection robustness of the framework.
Experimental Results
Experimental results demonstrate the effectiveness of the SGEIS framework:
- Gradient Boosting: Achieved a ROC-AUC score of 0.894.
- Graph-Based Models: Attained over 96% accuracy in identifying high-risk nodes within the grid.
Conclusion
The SmartGuard Energy Intelligence System represents a significant advancement in the fight against electricity theft. By integrating spatio-temporal and graph learning methodologies, it offers a scalable and practical solution for real-world implementation in smart grids. The high accuracy and improved interpretability of the framework position it as a robust tool for enhancing energy security in modern electrical infrastructures.