Intelligent Energy Meter Data Analysis and Anomaly Detection Combined with Big Data Technology

With the exponential growth of population and the widespread use of electronic and electrical equipment in daily life, energy production and management are facing unprecedented challenges. Smart energy meters (SMs) are an indispensable part of modern power systems (EPS), and their accuracy and reliability are crucial for the stable operation of the power system. However, due to the fact that SM is usually deployed on the user side, the operating environment is complex and variable, and there is currently a lack of effective technical management methods, which has led to an increasing demand for real-time monitoring and anomaly detection of SM operation status. To effectively address this challenge, this article proposes a SM data analysis and anomaly detection system that combines big data technology and deep learning (DL). The system collects massive data generated by SM during operation, and uses big data technology for preprocessing, analysis, and mining to obtain comprehensive information on the operating status of electricity meters. At the same time, combined with the DL algorithm, the system can automatically learn and recognize the normal and abnormal behavior patterns of the electricity meter, thereby achieving accurate anomaly detection. The experimental results show that the system can accurately analyze the operating status of SM and effectively detect abnormal behavior.