Smart Energy Guardian: A Hybrid Deep Learning Model for Detecting Fraudulent PV Generation
This addresses electricity theft detection for smart cities, but it is incremental as it builds on existing deep learning methods with a hybrid approach.
The paper tackles the problem of detecting fraudulent photovoltaic generation in smart grids by proposing a hybrid deep learning model that combines CNN, LSTM, and Transformer, achieving significant improvements in accuracy as validated with real-world data.
With the proliferation of smart grids, smart cities face growing challenges due to cyber-attacks and sophisticated electricity theft behaviors, particularly in residential photovoltaic (PV) generation systems. Traditional Electricity Theft Detection (ETD) methods often struggle to capture complex temporal dependencies and integrating multi-source data, limiting their effectiveness. In this work, we propose an efficient ETD method that accurately identifies fraudulent behaviors in residential PV generation, thus ensuring the supply-demand balance in smart cities. Our hybrid deep learning model, combining multi-scale Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Transformer, excels in capturing both short-term and long-term temporal dependencies. Additionally, we introduce a data embedding technique that seamlessly integrates time-series data with discrete temperature variables, enhancing detection robustness. Extensive simulation experiments using real-world data validate the effectiveness of our approach, demonstrating significant improvements in the accuracy of detecting sophisticated energy theft activities, thereby contributing to the stability and fairness of energy systems in smart cities.