Zibin Zheng, Yatao Yang, Xiangdong Niu, Hong-Ning Dai, Yuren Zhou, "Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids", IEEE Transactions on Industrial Informatics,vol. 14, no. 4, pp. 1606-1615, April 2018 Publication year: 2018

@ARTICLE{ZZheng:TII2018, 
	author={Z. Zheng and Y. Yang and X. Niu and H. 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}, 
	keywords={data analysis;load flow;neural nets;power consumption;power engineering computing;power system security;smart power grids;wide CNN model;deep CNN model;smart grids;abnormal electricity consumption pattern;novel electricity-theft detection method;deep CNN component;normal electricity usage;power grids;information flows;energy flows;massive data availability;data analysis;1D electricity consumption data;electricity theft nonperiodicity identification;normal electricity usage periodicity identification;Smart grids;Anomaly detection;Correlation;Support vector machines;Meters;Sensors;Neural networks;Convolutional neural networks (CNNs);deep learning;electricity-theft detection;machine learning;smart grids}, 
	doi={10.1109/TII.2017.2785963}, 
	ISSN={1551-3203}, 
	month={April},
}