Real-Time Monitoring and Simulation of Multi-User Electricity Metering Anomaly Data Based on Distributed System

Amidst the rapid development of smart grids and distributed energy systems, the volume and complexity of data within power systems have significantly increased, posing substantial challenges to traditional anomaly detection methods. To overcome these challenges, this study introduces a V-LSTM framework, an innovative approach that combines a variational autoencoder (VAE) with a long short-term memory (LSTM) network for anomaly detection in distributed power metering systems. The framework facilitates the extraction of features from multi-source data, generated by users' electricity consumption behavior, via VAE, while LSTM conducts a meticulous time-series analysis of these features to enable high-precision anomaly detection in complex datasets. VAE proficiently mitigates data complexity, preserving essential anomalous information, whereas LSTM augments the model's capacity to manage temporal dependencies. In empirical evaluations utilizing both public and proprietary datasets, the V-LSTM framework demonstrates superior performance in accuracy and AUC metrics, decisively surpassing traditional detection approaches. The experimental findings indicate that V-LSTM not only enhances the accuracy and dependability of power anomaly detection but also offers a novel technical trajectory for anomaly monitoring within the smart grid. This research outcome fosters the continued evolution of smart grid technology, providing more scientifically grounded management and decision support for power utilities and energy management entities.