LSTM‐Based Time Series Detection of Abnormal Electricity Usage in Smart Meters

In order to improve the accuracy and real-time performance of detecting abnormal behavior of smart meters, a time series prediction model based on Long Short-Term Memory (LSTM) is constructed, combining the sliding window mechanism and the residual dynamic thresholding strategy to realize the determination of abnormal behavior. The study covers data preprocessing, model structure design, system deployment, and visualization feedback, and optimizes the training performance by introducing Early Stopping, Dropout and learning rate adjustment. Comparison experiments are carried out based on real residential electricity consumption data, and the analysis shows that the LSTM model is better than traditional methods such as ARIMA, SVR and GRU in terms of prediction error and recognition accuracy, and it has strong sequence modeling capability and anomaly recognition stability.