Synthetic Theft Attacks and Long Short Term Memory-Based Preprocessing for Electricity Theft Detection Using Gated Recurrent Unit

Pamir, Nadeem Javaid, Saher Javaid, Muhammad Asif, Muhammad Umar Javed, Adamu Sani Yahaya and Sheraz Aslam
Additional contact information
Pamir: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Nadeem Javaid: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Saher Javaid: Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi City 923-1292, Japan
Muhammad Asif: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Muhammad Umar Javed: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Adamu Sani Yahaya: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Sheraz Aslam: Department of Electrical Engineering, Computer Engineering and Informatics, Cyprus University of Technology, Limassol 3036, Cyprus

Energies, 2022, vol. 15, issue 8, 1-20

Abstract: Electricity theft is one of the challenging problems in smart grids. The power utilities around the globe face huge economic loss due to ET. The traditional electricity theft detection (ETD) models confront several challenges, such as highly imbalance distribution of electricity consumption data, curse of dimensionality and inevitable effects of non-malicious factors. To cope with the aforementioned concerns, this paper presents a novel ETD strategy for smart grids based on theft attacks, long short-term memory (LSTM) and gated recurrent unit (GRU) called TLGRU. It includes three subunits: (1) synthetic theft attacks based data balancing, (2) LSTM based feature extraction, and (3) GRU based theft classification. GRU is used for drift identification. It stores and extracts the long-term dependency in the power consumption data. It is beneficial for drift identification. In this way, a minimum false positive rate (FPR) is obtained. Moreover, dropout regularization and Adam optimizer are added in GRU for tackling overfitting and trapping model in the local minima, respectively. The proposed TLGRU model uses the realistic EC profiles of the Chinese power utility state grid corporation of China for analysis and to solve the ETD problem. From the simulation results, it is exhibited that 1% FPR, 97.96% precision, 91.56% accuracy, and 91.68% area under curve for ETD are obtained by the proposed model. The proposed model outperforms the existing models in terms of ETD.

Keywords: theft attacks; long short term memory; gated recurrent unit; deep learning techniques; machine learning techniques; electricity theft detection; smart grids (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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