Detection of abnormal power consumption patterns of power users based on machine learning

With the development of China's power industry, the data of power consumption is increasing, and the illegal stealing of electricity by high-tech means is increasing. How to effectively detect abnormal power consumption behavior has become an important problem that power companies need to solve. Firstly, the principal component analysis (PCA) is used to reduce the dimension of the user's electricity consumption behavior to improve the efficiency of model detection; The abnormal detection of residential power consumption is realized by double algorithm judgment. Aiming at the problem that it is difficult to determine the abnormal proportion of LOF, DBSCAN is used to determine the abnormal value proportion, realize the optimization of LOF algorithm, and obtain the detection results of abnormal power consumption of residents; Then the Isolated Forest model is used to detect the abnormal electricity consumption of residents, and the users of abnormal electricity are selected; Finally, an example is given to verify the feasibility of the anomaly detection model, which improves the accuracy of abnormal power consumption detection.