A Convolution–Non-Convolution Parallel Deep Network for Electricity Theft Detection

Yiran Wang, Shuowei Jin () and Ming Cheng
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Yiran Wang: School of Materials Science and Engineering, Northeastern University, Shenyang 110819, China
Shuowei Jin: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Ming Cheng: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China

Sustainability, 2023, vol. 15, issue 13, 1-22

Abstract: This paper proposes a novel convolution–non-convolution parallel deep network (CNCP)-based method for electricity theft detection. First, the load time series of normal residents and electricity thieves were analyzed and it was found that, compared with the load time series of electricity thieves, the normal residents’ load time series present more obvious periodicity in different time scales, e.g., weeks and years; second, the load times series were converted into 2D images according to the periodicity, and then electricity theft detection was considered as an image classification issue; third, a novel CNCP-based method was proposed in which two heterogeneous deep neural networks were used to capture the features of the load time series in different time scales, and the outputs were fused to obtain the detection result. Extensive experiments show that, compared with some state-of-the-art methods, the proposed method can greatly improve the performance of electricity theft detection.

Keywords: smart grid; electricity theft detection; deep learning; parallel deep network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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