AUTHOR=Iftikhar Hasnain , Khan Nitasha , Raza Muhammad Amir , Abbas Ghulam , Khan Murad , Aoudia Mouloud , Touti Ezzeddine , Emara Ahmed TITLE=Electricity theft detection in smart grid using machine learning JOURNAL=Frontiers in Energy Research VOLUME=12 YEAR=2024 URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2024.1383090 DOI=10.3389/fenrg.2024.1383090 ISSN=2296-598X ABSTRACT=

Nowadays, electricity theft is a major issue in many countries and poses a significant financial loss for global power utilities. Conventional Electricity Theft Detection (ETD) models face challenges such as the curse of dimensionality and highly imbalanced electricity consumption data distribution. To overcome these problems, a hybrid system Multi-Layer Perceptron (MLP) approach with Gated Recurrent Units (GRU) is proposed in this work. The proposed hybrid system is applied to analyze and solve electricity theft using data from the Chinese National Grid Corporation (CNGC). In the proposed hybrid system, first, preprocess the data; second, balance the data using the k-means Synthetic Minority Oversampling Technique (SMOTE) technique; third, apply the GTU model to the extracted purified data; fourth, apply the MLP model to the extracted purified data; and finally, evaluate the performance of the proposed system using different performance measures such as graphical analysis and a statistical test. To verify the consistency of our proposed hybrid system, we use three different ratios for training and testing the dataset. The outcomes show that the proposed hybrid system for ETD is highly accurate and efficient compared to the other models like Alexnet, GRU, Bidirectional Gated Recurrent Unit (BGRU) and Recurrent Neural Network (RNN).