摘要：In order to reduce the non-technical loss and reduce the operating cost of the power company, an abnormal power consumption detection algorithm is proposed. The algorithm includes feature extraction, principal component analysis, grid processing, local outliers, and so on. Firstly, we extract several feature quantities that characterize the user's power consumption pattern, and map the X users to the two-dimensional plane by principal component analysis. Data visualization and easy to calculate local outliers, and grid processing techniques to filter out data points in low density regions. The algorithm is used to reduce the number of training samples in the power user data set, and to output the anomalies and probabilities of all users' behavior. The experimental results show that the use of the sorting only need to detect the anomaly of a few users can find a large number of abnormal users, significantly improve the efficiency of the algorithm.
