Journal of East China Normal University(Natural Science) ›› 2020, Vol. 2020 ›› Issue (5): 146-155.doi: 10.3969/j.issn.1000-5641.202091013

• Application of Data Platform • Previous Articles     Next Articles

Research on abnormal detection of daily loss rate based on a variational auto-encoder

ZHANG Guofang1, LIU Tongyu2, WEN Lili1, GUO Guo1, ZHOU Zhongxin3, YUAN Peisen2   

  1. 1. State Grid Sichuan Electric Power Company, Chengdu 610041, China;
    2. College of Information Science and Technology, Nanjing Agricultural University, Nanjing 210095, China;
    3. NARI Technology Co. Ltd., Nanjing 211106, China
  • Received:2020-08-14 Published:2020-09-24

Abstract: This paper adopts an anomaly detection algorithm based on a self-encoder to achieve anomaly detection of large-scale daily line loss rate data. A variational auto-encoder is a neural network that uses the backpropagation algorithm to make the output value approximately equal to the input value. It uses the auto-encoder to encode the original daily line loss rate time series and records the reconstruction possibility at each time point during the reconstruction process. When the reconstruction possibility is greater than a specified threshold, it is classified as anomaly data. In this paper, experiments were conducted on real daily line loss data. The test results show that the proposed algorithm for abnormal detection of daily line loss rate data based on an auto-encoder has good detection capability.

Key words: auto-encoder, anomaly detection, daily line loss rate

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