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Open AccessArticle

Detection of Non-Technical Losses Using SOSTLink and Bidirectional Gated Recurrent Unit to Secure Smart Meters

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Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
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Department of Electrical Engineering, University of Engineering and Technology Peshawar, Bannu 28100, Pakistan
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Department of Computer Science, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
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BIND Research Group, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
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School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad 44000, Pakistan
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Department of Computer Science, COMSATS University Islamabad, Wah 47000, Pakistan
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Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(9), 3151; https://doi.org/10.3390/app10093151
Received: 7 April 2020 / Revised: 24 April 2020 / Accepted: 28 April 2020 / Published: 30 April 2020
Energy consumption is increasing exponentially with the increase in electronic gadgets. Losses occur during generation, transmission, and distribution. The energy demand leads to increase in electricity theft (ET) in distribution side. Data analysis is the process of assessing the data using different analytical and statistical tools to extract useful information. Fluctuation in energy consumption patterns indicates electricity theft. Utilities bear losses of millions of dollar every year. Hardware-based solutions are considered to be the best; however, the deployment cost of these solutions is high. Software-based solutions are data-driven and cost-effective. We need big data for analysis and artificial intelligence and machine learning techniques. Several solutions have been proposed in existing studies; however, low detection performance and high false positive rate are the major issues. In this paper, we first time employ bidirectional Gated Recurrent Unit for ET detection for classification using real time-series data. We also propose a new scheme, which is a combination of oversampling technique Synthetic Minority Oversampling TEchnique (SMOTE) and undersampling technique Tomek Link: “Smote Over Sampling Tomik Link (SOSTLink) sampling technique”. The Kernel Principal Component Analysis is used for feature extraction. In order to evaluate the proposed model’s performance, five performance metrics are used, including precision, recall, F1-score, Root Mean Square Error (RMSE), and receiver operating characteristic curve. Experiments show that our proposed model outperforms the state-of-the-art techniques: logistic regression, decision tree, random forest, support vector machine, convolutional neural network, long short-term memory, hybrid of multilayer perceptron and convolutional neural network. View Full-Text
Keywords: non technical losses; gated recurrent unit; electricity theft; neural network; smart meter; supervised learning; artificial intelligence; advance meter infrastructure non technical losses; gated recurrent unit; electricity theft; neural network; smart meter; supervised learning; artificial intelligence; advance meter infrastructure
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Gul, H.; Javaid, N.; Ullah, I.; Qamar, A.M.; Afzal, M.K.; Joshi, G.P. Detection of Non-Technical Losses Using SOSTLink and Bidirectional Gated Recurrent Unit to Secure Smart Meters. Appl. Sci. 2020, 10, 3151.

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