Research on Electricity Anomaly Monitoring Based on Electricity Consumption Data Collection and Classification Modeling Methods

With the rapid expansion of power grids and increasing user demand, effectively identifying and monitoring abnormal electricity consumption have become crucial for ensuring grid stability and operational efficiency. Traditional anomaly detection methods often struggle with scalability and accuracy, particularly as the volume and density of electricity data grow. To address these challenges, this paper introduces a novel electricity anomaly monitoring framework that integrates real-time data acquisition and advanced classification modeling techniques. Our approach leverages a parallel classification algorithm designed to efficiently handle large datasets and detect anomalies with high accuracy. Key features of abnormal user data are extracted using information entropy, and electricity consumption data are continuously collected through a wireless network. The proposed method then preprocesses and classifies the data, applying a random forest model to detect anomalies and monitor usage patterns. Experimental results indicate that our approach significantly enhances both the accuracy and efficiency of electricity anomaly detection, demonstrating its robustness and potential for large-scale deployment in power grid systems.