Towards Intelligent Energy Security: A Unified Spatio-Temporal and Graph Learning Framework for Scalable Electricity Theft Detection in Smart Grids
Department of Chemical and Petroleum Engineering, Abiola Ajimobi Technical University（阿比奥拉·阿吉莫比技术大学化学与石油工程系）；Department of Computer Science, Abiola Ajimobi Technical University（阿比奥拉·阿吉莫比技术大学计算机科学系）
AbdulQoyum A. Olowookere, Usman A. Oguntola, Ebenezer. Leke Odekanle, Maridiyah A. Madehin, Aisha A. Adesope
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.
Spatio-Temporal Grid Intelligence: A Hybrid Graph Neural Network and LSTM Framework for Robust Electricity Theft Detection
Department Computer Science, Abiola Ajimobi Technical University（计算机科学系，阿比奥米博技术大学）；Department of Chemical and Petroleum Engineering, Abiola Ajimobi Technical University（化学与石油工程系，阿比奥米博技术大学）
Smart Energy Guardian: A Hybrid Deep Learning Model for Detecting Fraudulent PV Generation
University of Electronic Science and Technology of China（电子科技大学）；Monash University（墨尔本大学）；Shenzhen Institute for Advanced Study（深圳先进研究所）；Monash Energy Institute（墨尔本能源研究所）
EnsembleNTLDetect: An Intelligent Framework for Electricity Theft Detection in Smart Grid
Pune Institute of Computer Technology（浦那计算机技术学院）；College of Engineering, Guindy, Anna University（安娜大学金底工程学院）；Robert Bosch Centre for Data Science & Artificial Intelligence, IIT Madras（罗伯特博世数据科学与人工智能中心，马德拉斯印度理工学院）；Department of Electrical Engineering, IIT Bombay（印度理工学院孟买分校电气工程系）
Deep learning for spatio-temporal forecasting -- application to solar energy
HESAM Université（巴黎高科艺术与职业大学）；Conservatoire national des arts et métiers（法国国立工艺学院）；Simon Fraser University（西蒙弗雷泽大学）；Borealis AI（Borealis人工智能研究院）；Valeo AI（法雷奥人工智能研究院）；Sorbonne Université（索邦大学）；Criteo AI（科韬人工智能研究院）；Mines ParisTech（巴黎高等矿业学院）；EDF R&D（法国电力研发中心）；Université Rennes 1（雷恩第一大学）；INRIA（法国国家信息与自动化研究所）
TrustEnergy: A Unified Framework for Accurate and Reliable User-level Energy Usage Prediction
Florida State University（佛罗里达州立大学）；Massachusetts Institute of Technology（麻省理工学院）；University of California, Santa Barbara（加州大学圣巴巴拉分校）；University of Florida（佛罗里达大学）
Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses?
Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg（卢森堡大学安全、可靠性与信任跨学科中心）；University of Quebec in Montreal（魁北克大学蒙特利尔分校）；CHOICE Technologies Holding Sàrl
Identifying Irregular Power Usage by Turning Predictions into Holographic Spatial Visualizations
Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg（卢森堡大学安全、可靠性与信任跨学科中心）；University of Quebec in Montreal（魁北克大学蒙特利尔分校）；CHOICE Technologies Holding Sàrl
PowerAtlas: Feasibility-Aware Language Models for Electricity-Computing Co-Scheduling
Beijing University of Posts and Telecommunications（北京邮电大学）；State Grid Liaoning Electric Power Co., Ltd.（国网辽宁省电力有限公司）；Nanyang Technological University（南洋理工大学）
Deep Learning for Intelligent Demand Response and Smart Grids: A Comprehensive Survey
Vellore Institute of Technology（韦洛尔理工学院）；Pusan National University（釜山国立大学）；University of Oulu（奥卢大学）；Indian Institute of Technology Madras（印度理工学院马德拉斯分校）
