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System Development & Application
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 12
3717-3722,3727

Unsupervised abnormal power consumption detection via deep siamese autoregressive network

Li Qilin1
Yan Ping1
Su Xinyu2
Yuan Zhong2
Peng Dezhong2
Liu Yizhi2
1. Marketing Service Center of State Grid Sichuan Electric Power Corporation, Chengdu 610045, China
2. College of Computer Science, Sichuan University, Chengdu 610065, China
DOI: 10.19734/j.issn.1001-3695.2023.07.0337

Abstract

Abnormal electricity consumption detection aims to identify electricity consumption behaviors that do not conform to normal electricity consumption patterns or violate electricity consumption contracts. To address the issues of existing reconstruction-based detection methods relying on labeled normal samples and failing to capture complex time dependencies, this paper proposed an unsupervised abnormal electricity consumption detection model based on deep siamese autoregressive networks(DSAD), which used two siamese autoregressive subnetworks to independently reconstruct the unlabeled input data, and then combined the reconstruction errors of the two subnetworks to predict the normal samples in the data, and utilized multi-head self-attention mechanism to effectively capture complex features such as time dependency, periodicity and randomness. The results obtained from experiments on large-scale time series datasets and real electricity consumption datasets from state grid show that the proposed method achieves better detection performance in terms of AUC and AP.

Foundation Support

国网四川省电力公司科技项目(521997230015)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.07.0337
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 12
Section: System Development & Application
Pages: 3717-3722,3727
Serial Number: 1001-3695(2023)12-030-3717-06

Publish History

[2023-12-05] Printed Article

Cite This Article

李琪林, 严平, 宿欣宇, 等. 基于深度孪生自回归网络的无监督异常用电检测 [J]. 计算机应用研究, 2023, 40 (12): 3717-3722,3727. (Li Qilin, Yan Ping, Su Xinyu, et al. Unsupervised abnormal power consumption detection via deep siamese autoregressive network [J]. Application Research of Computers, 2023, 40 (12): 3717-3722,3727. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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