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.
Abstract (translated)
窃电与非技术损耗（NTLs）仍是现代智能电网面临的关键挑战，不仅造成重大经济损失，还损害了电网可靠性。本研究提出了智能卫士能源智能系统（SGEIS），这是一个用于窃电检测与智能能源监测的集成人工智能框架。该系统结合了监督机器学习、基于深度学习的时间序列建模、非侵入式负载监测（NILM）以及基于图的学习，以捕捉时空用电模式。研究开发了全面的数据处理流程，涵盖特征工程、多尺度时间分析和基于规则的异常标注。采用深度学习模型（包括长短期记忆网络（LSTM）、时间卷积网络（TCN）和自编码器）检测异常用电模式，同时利用随机森林、梯度提升、XGBoost和LightGBM等集成学习方法进行分类。为模拟电网拓扑与空间依赖性，应用图神经网络（GNNs）识别互联节点间的关联异常。NILM模块通过从聚合信号中分解电器级用电量，增强了系统可解释性。实验结果表明系统性能优异：梯度提升模型的ROC-AUC值达0.894，基于图的模型在识别高风险节点时准确率超过96%。该混合框架通过整合时间、统计与空间智能，提升了检测鲁棒性。总体而言，SGEIS为窃电检测提供了可扩展且实用的解决方案，具备高精度、强可解释性，并在实际智能电网部署中展现出巨大潜力。
URL
https://arxiv.org/abs/2604.03344
