融合时空与图学习，提升智能电网窃电检测精度与可解释性
Towards Intelligent Energy Security: A Unified Spatio-Temporal and Graph Learning Framework for Scalable Electricity Theft Detection in Smart Grids
- 结合时序、统计与拓扑关系，构建多模态检测框架
- 梯度提升模型达0.894的ROC-AUC，图模型识别高风险节点准确率超96%
- 支持设备级用电分解，适合电网运维与反窃电系统部署
电力窃漏和非技术损耗（NTLs）是现代智能电网中的关键挑战，造成重大经济损失并威胁电网可靠性。本文提出智能电网能源安全系统（SGEIS），一种集成人工智能的窃电检测与智能能源监控框架。该系统融合监督学习、基于深度学习的时间序列建模、非侵入式负荷监测（NILM）及图学习，以捕捉用电行为的时空模式。开发了包含特征工程、多尺度时间分析和规则化异常标注的数据处理流程。采用LSTM、TCN与自编码器等深度模型检测异常用电模式，同时使用随机森林、梯度提升、XGBoost与LightGBM等集成方法进行分类。通过图神经网络（GNN）建模电网拓扑与空间依赖性，识别互联节点间的关联异常。NILM模块通过分解聚合信号实现设备级用电可解释性。实验表明，梯度提升模型取得0.894的ROC-AUC，图模型在识别高风险节点上准确率超过96%。混合框架通过融合时序、统计与空间智能，显著提升检测鲁棒性。整体上，SGEIS提供了一种可扩展、实用的窃电检测方案，具备高精度、强可解释性，具有实际部署潜力。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。