Aiming at the problem of theft and leakage diagnosis for electricity consumers,based on real and massive user electricity consumption data,and machine learning technology,data cleaning and feature selection are carried out on the sample data.Power users are dynamically grouped based on their electricity consumption characteristics and multiple factors related to power stealing,ensuring the pertinence and adaptability of theft and leakage analysis.Build a classification model using support vector machines algorithm based on Gaussian kernel function,visualize the classification results,and simulate the algorithm through five fold cross validation to analyze the reliability and stability of the model.Finally,the effectiveness of this method is further verified through on-site verification.
support vector machinepower stealingintelligent diagnosis
NETL
NSTL
万方数据
维普
