Anomaly Detection and Data Mining Techniques in the Electric Power Marketing Information Platform

With the continuous expansion of the global electricity market and the rapid development of power information technology, the power marketing information platform has become the core support for the operation and management of the power industry. This platform not only efficiently processes massive transaction data, user behavior records, and power grid operation status information, but these data resources are like treasure trove, containing enormous potential for optimizing services, improving efficiency, predicting market trends, and enhancing power grid stability. However, if data anomalies and potential risks such as fraudulent behavior, equipment failure warning, and abnormal load fluctuations are not detected and dealt with in a timely manner, they will pose a threat to the economic benefits of power enterprises, the safe and stable operation of the power grid, and even the public interest. In response to the above issues, this article innovatively proposes an anomaly detection system based on deep integration of data mining (DM) and machine learning (ML) techniques. This system integrates multiple advanced algorithms to deeply mine and intelligently analyze big data in the power marketing information platform. The experimental results show that the system can efficiently identify and warn of various abnormal patterns, significantly improving the risk prevention and control capabilities and decision support level of power enterprises.