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Open AccessArticle

LSTM and Bat-Based RUSBoost Approach for Electricity Theft Detection

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Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad 44000, Pakistan
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Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
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Department of Computer Science, University of Engineering & Technology, New Campus, Lahore 54000, Pakistan
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Department of Electrical Engineering, University of Engineering and Technology Peshawar, Bannu 28100, Pakistan
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Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(12), 4378; https://doi.org/10.3390/app10124378
Received: 30 April 2020 / Revised: 15 June 2020 / Accepted: 21 June 2020 / Published: 25 June 2020
(This article belongs to the Special Issue Artificial Intelligence for Smart Systems)
The electrical losses in power systems are divided into non-technical losses (NTLs) and technical losses (TLs). NTL is more harmful than TL because it includes electricity theft, faulty meters and billing errors. It is one of the major concerns in the power system worldwide and incurs a huge revenue loss for utility companies. Electricity theft detection (ETD) is the mechanism used by industry and academia to detect electricity theft. However, due to imbalanced data, overfitting issues and the handling of high-dimensional data, the ETD cannot be applied efficiently. Therefore, this paper proposes a solution to address the above limitations. A long short-term memory (LSTM) technique is applied to detect abnormal patterns in electricity consumption data along with the bat-based random under-sampling boosting (RUSBoost) technique for parameter optimization. Our proposed system model uses the normalization and interpolation methods to pre-process the electricity data. Afterwards, the pre-processed data are fed into the LSTM module for feature extraction. Finally, the selected features are passed to the RUSBoost module for classification. The simulation results show that the proposed solution resolves the issues of data imbalancing, overfitting and the handling of massive time series data. Additionally, the proposed method outperforms the state-of-the-art techniques; i.e., support vector machine (SVM), convolutional neural network (CNN) and logistic regression (LR). Moreover, the F1-score, precision, recall and receiver operating characteristics (ROC) curve metrics are used for the comparative analysis. View Full-Text
Keywords: non-technical losses; electricity theft; smart meter; random under-sampling; imbalanced data; parameter tuning non-technical losses; electricity theft; smart meter; random under-sampling; imbalanced data; parameter tuning
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MDPI and ACS Style

Adil, M.; Javaid, N.; Qasim, U.; Ullah, I.; Shafiq, M.; Choi, J.-G. LSTM and Bat-Based RUSBoost Approach for Electricity Theft Detection. Appl. Sci. 2020, 10, 4378. https://doi.org/10.3390/app10124378

AMA Style

Adil M, Javaid N, Qasim U, Ullah I, Shafiq M, Choi J-G. LSTM and Bat-Based RUSBoost Approach for Electricity Theft Detection. Applied Sciences. 2020; 10(12):4378. https://doi.org/10.3390/app10124378

Chicago/Turabian Style

Adil, Muhammad; Javaid, Nadeem; Qasim, Umar; Ullah, Ibrar; Shafiq, Muhammad; Choi, Jin-Ghoo. 2020. "LSTM and Bat-Based RUSBoost Approach for Electricity Theft Detection" Appl. Sci. 10, no. 12: 4378. https://doi.org/10.3390/app10124378

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