Predictive Data Analytics for Electricity Fraud Detection Using Tuned CNN Ensembler in Smart Grid

Nasir Ayub, Usman Ali, Kainat Mustafa, Syed Muhammad Mohsin and Sheraz Aslam ()
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Nasir Ayub: Faculty of Computing, Department of Software Engineering, Capital University of Science and Technology, Islamabad 44000, Pakistan
Usman Ali: Department of Computing, Riphah International University, Faisalabad 45320, Pakistan
Kainat Mustafa: Department of Computer Science, Virtual University of Pakistan, Lahore 55150, Pakistan
Syed Muhammad Mohsin: Department of Computer Science, COMSATS University Islamabad, Islamabad 45550, Pakistan
Sheraz Aslam: Department of Electrical Engineering, Computer Engineering, and Informatics, Cyprus University of Technology, Limassol 3036, Cyprus, Cyprus

Forecasting, 2022, vol. 4, issue 4, 1-13

Abstract: In the smart grid (SG), user consumption data are increasing very rapidly. Some users consume electricity legally, while others steal it. Electricity theft causes significant damage to power grids, affects power supply efficiency, and reduces utility revenues. This study helps utilities reduce the problems of electricity theft, inefficient electricity monitoring, and abnormal electricity consumption in smart grids. To this end, an electricity theft dataset from the state grid corporation of China (SGCC) is employed and this study develops a novel model, a mixture of convolutional neural network and gated recurrent unit (CNN-GRU), for automatic power theft detection. Moreover, the hyperparameters of the proposed model are tuned using a meta-heuristic method, the cuckoo search (CS) algorithm. The class imbalance problem is solved using the synthetic minority oversampling technique (SMOTE). The clean data are trained and then tested with the proposed classification. Extensive simulations are performed based on real energy consumption data. The simulated results show that the proposed theft detection model (CNN-GRU-CS) solved the theft classification problem better than other approaches in terms of effectiveness and accuracy by 10% on average. The calculated accuracy of the proposed method is 92% and the precision is 94%.

Keywords: data analytics; deep learning; electricity fraud detection; optimization; smart grid (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2022
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