Study on abnormal power usage detection of grid users based on improved neural network

This paper presents an AdaBoost-DNN (Adaptive Boosting-Deep Neural Network) model for the detection of anomalous electricity consumption in power grid users.Initially, the k-means SMOTE (Synthetic Minority Oversampling Technique) technique is employed to enhance the sample set of the original anomalous consumption data to address the issue of data imbalance.Subsequently, an ensemble learning model based on AdaBoost-DNN is designed for the detection of anomalous consumption.To validate the effectiveness and superiority of the proposed AdaBoost-DNN model, comparative experiments are conducted with three traditional algorithms.