Anomaly detection and identification of power consumption data based on LOF and isolation forest

With the rapid development of the energy Internet, the power industry has ushered in the era of power big data. Through dynamic monitoring and analysis of all kinds of data in power grid operation, potential risks can be found effectively and timely, and the safety of power grid operation can be improved. As an important part of power big data, user-side power consumption data provides strong support for managers to visually display users' abnormal power consumption behaviors. It provides a theoretical basis for better and faster solution to the behavior of electricity leakage due to equipment failure or personal behavior. Based on this background, this paper first uses k-means clustering algorithm to classify electricity consumption data according to user usage habits. This method can effectively avoid misjudgment and missing judgment of electricity data. Finally, the method of combining LOF and isolation forest is used to realize the anomaly detection of user side power consumption data. By comparing the single algorithm (LOF and isolation forest), it is proved that the anomaly monitoring model combined with the two algorithms can better identify the abnormal power consumption data at the user side.