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, Jong-Myon Kim

Research output: Contribution to journalArticlepeer-review

245 Citations (Scopus)


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.
Original languageEnglish
Issue number17
Publication statusPublished (in print/issue) - 28 Aug 2019


  • smart grid
  • electricity
  • energy
  • non-technical loss
  • data analysis
  • machine learning
  • convolutional neural network (CNN)
  • Long Short-Term Memory (LSTM)


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