Abnormal Detection of Power Data Based on SE-CNN Model of Power Stealing Behavior
In order to prevent and control power stealing behavior effectively,an abnormal detection and analysis method of power data based on(SE-CNN)power stealing behavior model is designed on the basis of the construction of power consump-tion trend decline index.Based on the CNN model,SENet is introduced to adjust the importance of the feature channels,so as to effectively improve the utilization rate of the channels.The experiem ental results show that the AUC value of SE-CNN model reaches 0.999,the detection efficiency is high,and the SE-CNN model can be applied to complex power grid environ-ment.Compared with support vector machine(SVM),XGBoost and CNN,SE-CNN model has good evaluation indexes,effec-tively reduces the experimental influence brought by invalid features,realizes feature fusion in local areas,and makes experi-mental data quickly reach the fitting state.
power stealing beheaviorimproved convolutional neural networkabnormal detectionevaluation index