Sample Enhancement Method for Electricity Theft Detection Based on Sample Convolution Interactive Learning
In order to detect electricity theft,there are many studies and applications,but the existing research on electricity theft detection fails to effectively solve the problem of unbalanced sample distribution.In order to solve this problem,a sample enhancement method for electricity theft detection based on sample convolution interactive learning is proposed.Firstly,the training set after data preprocessing is increased by the sample convolution interactive learning method.Then,the sample enhanced training set is input into a three-layer convolutional neural network model for feature extraction.Finally,a two-layer fully connected layer is used to output the detection results,and common evaluation indicators are used to verify the effectiveness of the sample enhancement mechanism.Simulation results on the State Grid Corporation of China(SGCC)dataset show that the proposed TSCINet-CNN model achieves excellent results of 0.8822,0.5445 and 0.5560 on the area under the curve,F1-score and MAP of 70%of the training set,respectively.
NETL
NSTL
万方数据
