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

Md. Nazmul Hasan, Rafia Nishat Toma, Abdullah-Al Nahid, M M Manjurul Islam and Jong-Myon Kim
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Md. Nazmul Hasan: Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh
Rafia Nishat Toma: Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh
Abdullah-Al Nahid: Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh
M M Manjurul Islam: School of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, South Korea
Jong-Myon Kim: School of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, South Korea

Energies, 2019, vol. 12, issue 17, 1-18

Abstract: 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.

Keywords: smart grid; electricity; energy; non-technical loss; data analysis; machine learning; convolutional neural network (CNN); long short-term memory (LSTM) (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: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (27)

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