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Energy Theft Detection Model Based on VAE-GAN for Imbalanced Dataset

Author

Listed:
  • Youngghyu Sun

    (Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of Korea)

  • Jiyoung Lee

    (Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of Korea)

  • Soohyun Kim

    (Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of Korea)

  • Joonho Seon

    (Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of Korea)

  • Seongwoo Lee

    (Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of Korea)

  • Chanuk Kyeong

    (Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of Korea)

  • Jinyoung Kim

    (Department of Electronic Convergence Engineering, Kwangwoon University, Seoul 01897, Republic of Korea)

Abstract

Energy theft causes a lot of economic losses every year. In the practical environment of energy theft detection, it is required to solve imbalanced data problem where normal user data are significantly larger than energy theft data. In this paper, a variational autoencoder-generative adversarial network (VAE-GAN)-based energy theft-detection model is proposed to overcome the imbalanced data problem. In the proposed model, the VAE-GAN generates synthetic energy theft data with the features of real energy theft data for augmenting the energy theft dataset. The obtained balanced dataset is applied to train a detector which is designed as one-dimensional convolutional neural network. The proposed model is simulated on the practical dataset for comparing with various generative models to evaluate their performance. From simulation results, it is confirmed that the proposed model outperforms the other existing models. Additionally, it is shown that the proposed model is also very useful in the environments of extreme data imbalance for a wide variety of applications by analyzing the performance of detector according to the balance rate.

Suggested Citation

  • Youngghyu Sun & Jiyoung Lee & Soohyun Kim & Joonho Seon & Seongwoo Lee & Chanuk Kyeong & Jinyoung Kim, 2023. "Energy Theft Detection Model Based on VAE-GAN for Imbalanced Dataset," Energies, MDPI, vol. 16(3), pages 1-13, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1109-:d:1041050
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    References listed on IDEAS

    as
    1. Md. Nazmul Hasan & Rafia Nishat Toma & Abdullah-Al Nahid & M M Manjurul Islam & Jong-Myon Kim, 2019. "Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach," Energies, MDPI, vol. 12(17), pages 1-18, August.
    2. Zeeshan Aslam & Nadeem Javaid & Ashfaq Ahmad & Abrar Ahmed & Sardar Muhammad Gulfam, 2020. "A Combined Deep Learning and Ensemble Learning Methodology to Avoid Electricity Theft in Smart Grids," Energies, MDPI, vol. 13(21), pages 1-24, October.
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