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Open AccessArticle

Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach

1
Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh
2
School of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, South Korea
*
Author to whom correspondence should be addressed.
Energies 2019, 12(17), 3310; https://doi.org/10.3390/en12173310
Received: 24 July 2019 / Revised: 25 August 2019 / Accepted: 26 August 2019 / Published: 28 August 2019
(This article belongs to the Special Issue Cybersecurity in Smartgrids)
Among an electricity provider’s non-technical losses, electricity theft has the most severe and dangerous effects. Fraudulent electricity consumption decreases the supply quality, increases generation load, causes legitimate consumers to pay excessive electricity bills, and affects the overall economy. The adaptation of smart grids can significantly reduce this loss through data analysis techniques. The smart grid infrastructure generates a massive amount of data, including the power consumption of individual users. Utilizing this data, machine learning and deep learning techniques can accurately identify electricity theft users. In this paper, an electricity theft detection system is proposed based on a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) architecture. CNN is a widely used technique that automates feature extraction and the classification process. Since the power consumption signature is time-series data, we were led to build a CNN-based LSTM (CNN-LSTM) model for smart grid data classification. In this work, a novel data pre-processing algorithm was also implemented to compute the missing instances in the dataset, based on the local values relative to the missing data point. Furthermore, in this dataset, the count of electricity theft users was relatively low, which could have made the model inefficient at identifying theft users. This class imbalance scenario was addressed through synthetic data generation. Finally, the results obtained indicate the proposed scheme can classify both the majority class (normal users) and the minority class (electricity theft users) with good accuracy. View Full-Text
Keywords: smart grid; electricity; energy; non-technical loss; data analysis; machine learning; convolutional neural network (CNN); long short-term memory (LSTM) smart grid; electricity; energy; non-technical loss; data analysis; machine learning; convolutional neural network (CNN); long short-term memory (LSTM)
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MDPI and ACS Style

Hasan, M.N.; Toma, R.N.; Nahid, A.-A.; Islam, MM M.; Kim, J.-M. Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach. Energies 2019, 12, 3310. https://doi.org/10.3390/en12173310

AMA Style

Hasan MN, Toma RN, Nahid A-A, Islam MMM, Kim J-M. Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach. Energies. 2019; 12(17):3310. https://doi.org/10.3390/en12173310

Chicago/Turabian Style

Hasan, Md. N.; Toma, Rafia N.; Nahid, Abdullah-Al; Islam, M M M.; Kim, Jong-Myon. 2019. "Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach" Energies 12, no. 17: 3310. https://doi.org/10.3390/en12173310

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