Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids
By Zibin Zheng, Yatao Yang, Xiangdong Niu, Hongning Dai, Yuren Zhou. IEEE Transactions on Industrial Informatics, 2018
643 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Control and Systems Engineering, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/e8f05ecda7b179fd3847cdebd28afad59a01625c
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Citations in the corpus
- IoT in Smart Cities: A Survey of Technologies, Practices and Challenges (2021, background): “Different powerline events Homogeneous (Electric power) Electricitytheftdetection CNN [131] Cloud Classification—Theft detection for abnormal patterns of consumption”
- Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach (2019, methodology): “Values We employed the preprocessing method used in [7].”
- Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives (2021, background): “[67] propose a CNN-based solution, which helps mainly in identifying the non-periodicity of energy theft and periodicity of normal energy consumption using 2D representations of power consumption signals.”
- A Review on Digital Twin Technology in Smart Grid, Transportation System and Smart City: Challenges and Future (2023, methodology): “In [76], a deep-learning-based CNN algorithm is proposed for network intrusion detection for the SCADA system.”