Electricity Theft Detection Techniques

Neueste Paper

• Privacy-Preserving Generation Fraud Detection for Distributed Photovoltaic Systems: A Solar Irradiance-Fused Federated Learning Framework

  Xiaolu Chen, Chenghao Huang, Yanru Zhang, Hao Wang · 19. Mai 2026

  The wide adoption of residential photovoltaic (PV) systems introduces new challenges for generation fraud detection (FD). Unlike traditional electricity theft detection, which focuses on electricity consumption-side behavior, PV generation fraud detection (PVG-FD) is complicated by the inherent inte…

• Transformer autoencoder with local attention for sparse and irregular time series with application on risk estimation

  Panteleimon Rodis · 12. Mai 2026

  This paper introduces a framework specifically designed for sparse and irregular time series {risk estimation}. It is based on a Transformer Autoencoder with local attention, which leverages the powerful pattern identification capabilities of transformers complemented by traditional data cleaning an…

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

  AbdulQoyum A. Olowookere, Usman A. Oguntola, Ebenezer. Leke Odekanle, Maridiyah A. Madehin, Aisha A. Adesope · 7. April 2026

  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 elec…

• Spatio-Temporal Grid Intelligence: A Hybrid Graph Neural Network and LSTM Framework for Robust Electricity Theft Detection

  Adewale U. Oguntola, Olowookere A. AbdulQoyum, Adebukola M. Madehin, Adekemi A. Adetoro · 24. März 2026

  Electricity theft, or non-technical loss (NTL), presents a persistent threat to global power systems, driving significant financial deficits and compromising grid stability. Conventional detection methodologies, predominantly reactive and meter-centric, often fail to capture the complex spatio-tempo…

• Towards Secure and Scalable Energy Theft Detection: A Federated Learning Approach for Resource-Constrained Smart Meters

  Diego Labate, Dipanwita Thakur, Giancarlo Fortino · 19. Februar 2026

  Energy theft poses a significant threat to the stability and efficiency of smart grids, leading to substantial economic losses and operational challenges. Traditional centralized machine learning approaches for theft detection require aggregating user data, raising serious concerns about privacy and…

• Anomaly Detection with Machine Learning Algorithms in Large-Scale Power Grids

  Marc Gillioz, Guillaume Dubuis, \'Etienne Voutaz, Philippe Jacquod · 12. Februar 2026

  We apply several machine learning algorithms to the problem of anomaly detection in operational data for large-scale, high-voltage electric power grids. We observe important differences in the performance of the algorithms. Neural networks typically outperform classical algorithms such as k-nearest …

• Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning

  Ratun Rahman, Pablo Moriano, Samee U. Khan, Dinh C. Nguyen · 4. November 2025

  Electric load forecasting is essential for power management and stability in smart grids. This is mainly achieved via advanced metering infrastructure, where smart meters (SMs) record household energy data. Traditional machine learning (ML) methods are often employed for load forecasting, but requir…