The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids
Abstract
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.
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(2019) · NORA - Norwegian Open Research Archives · Cited by 291
Metadata
- Authors
- Zheng, Z., Yang, Y., Niu, X., Dai, H.-N., & Zhou, Y.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2018-04
- Volume
- 14
- Issue
- 4
- Pages
- 1606-1615
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2776990447
- Cited by
- 788
- References
- 29
- FWCI
- 14.10
- Type
- journal article
- Language
- en
- Open access
- closed
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