Towards Big Data Electricity Theft Detection Based on Improved RUSBoost Classifiers in Smart Grid

Rehan Akram, Nasir Ayub, Imran Khan, Fahad R. Albogamy, Gul Rukh, Sheraz Khan, Muhammad Shiraz and Kashif Rizwan
Additional contact information
Rehan Akram: Department of Computer Science, Federal Urdu University of Arts, Science and Technology Islamabad, Islamabad 44000, Pakistan
Nasir Ayub: Department of Computer Science, Federal Urdu University of Arts, Science and Technology Islamabad, Islamabad 44000, Pakistan
Imran Khan: Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan
Fahad R. Albogamy: Computer Sciences Program, Turabah University College, Taif University, P.O. Box 11099, Taif 26571, Saudi Arabia
Gul Rukh: Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan
Sheraz Khan: Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan
Muhammad Shiraz: Department of Computer Science, Federal Urdu University of Arts, Science and Technology Islamabad, Islamabad 44000, Pakistan
Kashif Rizwan: Department of Computer Science, Federal Urdu University of Arts, Science and Technology Islamabad, Islamabad 44000, Pakistan

Energies, 2021, vol. 14, issue 23, 1-17

Abstract: The advent of the new millennium, with the promises of the digital age and space technology, favors humankind in every perspective. The technology provides us with electric power and has infinite use in multiple electronic accessories. The electric power produced by different sources is distributed to consumers by the transmission line and grid stations. During the electric transmission from primary sources, there are various methods by which to commit energy theft. Energy theft is a universal electric problem in many countries, with a possible loss of billions of dollars for electric companies. This energy contention is deep rooted, having so many root causes and rugged solutions of a technical nature. Advanced Metering Infrastructure (AMI) is introduced with no adequate results to control and minimize electric theft. Until now, so many techniques have been applied to overcome this grave problem of electric power theft. Many researchers nowadays use machine learning algorithms, trying to combat this problem, giving better results than previous approaches. Random Forest (RF) classifier gave overwhelmingly good results with high accuracy. In our proposed solution, we use a novel Convolution Neural Network (CNN) with RUSBoost Manta Ray Foraging Optimization (rus-MRFO) and RUSBoost Bird Swarm Algorithm (rus-BSA) models, which proves to be very innovative. The accuracy of our proposed approaches, rus-MRFO and rus-BSA, are 91.5% and a 93.5%, respectively. The proposed techniques have shown promising results and have strong potential to be applied in future.

Keywords: smart grid; electricity theft; advanced metering infrastructure; RUSBoost; manta ray foraging optimization; bird swarm algorithm (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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