Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids

Zibin ZHENG, Yatao YANG, Xiangdong NIU, Hong Ning DAI*, Yuren ZHOU

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

168 Citations (Scopus)


Electricity theft is harmful to power grids. Integrating information flows with energy flows, smart grids can help to solve the problem of electricity theft owning to the availability of massive data generated from smart grids. The data analysis on the data of smart grids is helpful in detecting electricity theft because of the abnormal electricity consumption pattern of energy thieves. However, the existing methods have poor detection accuracy of electricity theft since most of them were conducted on one-dimensional (1-D) electricity consumption data and failed to capture the periodicity of electricity consumption. In this paper, we originally propose a novel electricity-theft detection method based on wide and deep convolutional neural networks (CNN) model to address the above concerns. In particular, wide and deep CNN model consists of two components: the wide component and the deep CNN component. The deep CNN component can accurately identify the nonperiodicity of electricity theft and the periodicity of normal electricity usage based on 2-D electricity consumption data. Meanwhile, the wide component can capture the global features of 1-D electricity consumption data. As a result, wide and deep CNN model can achieve the excellent performance in electricity-theft detection. Extensive experiments based on realistic dataset show that wide and deep CNN model outperforms other existing methods.

Original languageEnglish
Pages (from-to)1606-1615
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Issue number4
Early online date21 Dec 2017
Publication statusPublished - Apr 2018
Externally publishedYes

Bibliographical note

Funding Information:
Manuscript received August 31, 2017; revised November 27, 2017; accepted December 11, 2017. Date of publication December 21, 2017; date of current version April 3, 2018. This paper was supported in part by the National Key Research and Development Program (2016YFB1000101), in part by the National Natural Science Foundation of China under Grant 61722214 and Grant 61472338, in part by the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (2016ZT06D211), and in part by the Pearl River S & T Nova Program of Guangzhou (201710010046). Paper No. TII-17-2030. (Corresponding author: Hong-Ning Dai.) Z. Zheng, Y. Yang, X. Niu and Y. Zhou are with the School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China (e-mail: zhzibin@mail.sysu.edu.cn; yangyt9@mail2.sysu.edu.cn; niuxd@mail2.sysu.edu.cn; zhouyuren@mail.sysu.edu.cn).

Publisher Copyright:
© 2005-2012 IEEE.


  • Convolutional neural networks (CNNs)
  • deep learning
  • electricity-theft detection
  • machine learning
  • smart grids


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