Abstract

Electricity theft can be harmful to power grid suppliers and cause economic losses. 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 & Deep Convolutional Neural Networks (CNN) model to address the above concerns. In particular, Wide & Deep CNN model consists of two components: the Wide component and the Deep CNN component. The Deep CNN component can accurately identify the non-periodicity of electricity-theft and the periodicity of normal electricity usage based on two dimensional (2-D) electricity consumption data. Meanwhile, the Wide component can capture the global features of 1-D electricity consumption data. As a result, Wide & Deep CNN model can achieve the excellent performance in electricity-theft detection. Extensive experiments based on realistic dataset show that Wide & Deep CNN model outperforms other existing methods.

Keywords

• Convolutional neural networks
• Deep learning
• Electricity-theft detection
• Machine learning
• Smart grids

Bibtex

@ARTICLE{ZZheng:TII18, 
	author={Z. Zheng and Y. Yang and X. Niu and H.-N. Dai and Y. Zhou}, 
	journal={IEEE Transactions on Industrial Informatics}, 
	title={Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids}, 
	year={2018}, 
	volume={14}, 
	number={4}, 
	pages={1606-1615}, 
	doi={10.1109/TII.2017.2785963}, 
	month={April},
}

Note:

We publish the dataset used in this paper to promote the research in this area. The dataset can be found through this link: https://github.com/henryRDlab/ElectricityTheftDetection/