A method for identifying abnormal behavior of electricity consumption based on support vector machine optimization based on genetic algorithm

Abstract

Optimizing the support vector machine (SVM) classification model based on genetic algorithm (GA) can significantly improve the classification accuracy of traditional SVM. In order to solve the problem of abnormal power consumption classification in the public transformer area, this paper analyzes the classification methods based on SVM and GA-SVM. This paper extracts 15 eigenvalues, including parameters such as voltage, current, power, phase angle, power factor, etc., based on the research objects of 358 different users' electricity consumption information in multiple stations in Baoding, and divides the corresponding electricity consumption behaviors. There are 7 kinds of classification results, and then the data is classified based on SVM, and GA is used to find the optimal penalty factor C parameter and the g parameter in the kernel function, and the kernel function is the radial basis function (RBF). Finally, the above parameters are input into the GA-SVM model. The experimental results show that the trained model can quickly and accurately classify the abnormal power consumption behavior of the public transformer station area, and a new exploration of the abnormal behavior recognition algorithm in the public transformer station area has been carried out to ensure the power consumption quality of the public transformer station area. Further standardize the order of electricity supply.