Anomaly Detection for Power Consumption Data based on Isolated Forest

Abstract

Recently, the user-side data of power grids gradually exhibit massive data and high complexity features. The traditional power anomaly detection model has been difficult to meet the existing requirements. In recent years, the neural networks and machine learning methods that are widely used in anomaly detection, but all of these methods have a high demand for training samples and cannot be well applied when missing sample tags on the power data set. This paper designs an unsupervised power data anomaly detection model mainly based on the isolated forest algorithm. The model includes modules for feature extraction, feature reduction, and isolated forest computing. The research results show that using this model to detect abnormal power usage data can process large amounts of data in a short time, but also can accommodate the lack of training samples and can better meet the practical needs of the power sector.