We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
A Convolution–Non-Convolution Parallel Deep Network for Electricity Theft Detection.
- Authors
Wang, Yiran; Jin, Shuowei; Cheng, Ming
- 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.
- Subjects
ARTIFICIAL neural networks; IMAGE recognition (Computer vision); THEFT; TIME series analysis
- Publication
Sustainability (2071-1050), 2023, Vol 15, Issue 13, p10127
- ISSN
2071-1050
- Publication type
Article
- DOI
10.3390/su151310127