Smart Energy Guardian: A Hybrid Deep Learning Model for Detecting Fraudulent PV Generation

TL;DR

This paper introduces a hybrid deep learning model combining CNN, LSTM, and Transformer architectures to improve detection of fraudulent PV energy generation, addressing complex temporal dependencies and multi-source data integration in smart grids.

Contribution

It presents a novel hybrid deep learning approach with a data embedding technique for enhanced electricity theft detection in residential PV systems.

Findings

Significant accuracy improvements over traditional methods.

Effective detection of complex energy theft behaviors.

Robust performance with real-world data simulations.

Abstract

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.