基于DeepSMOTE和深度聚类的专变用户负荷辨识方法研究OA

According to the problem of class imbalance in the load data of specialized variable users and the limitations of traditional clustering methods in dealing with high-dimensional time series data,it proposes a method for identifying unbalanced load data of spe-cialized variable users based on deep oversampling(DeepSMOTE)and deep clustering to enhance the ability of special transformer users load identification.Firstly,it proposes a load data expansion method based on DeepSMOTE to achieve the balance of the dedica-ted transformer user electricity consumption data set.Then,it uses the deep clustering algorithm to cluster the load curve of the special transformer user,and extracts the typical load curve of the special transformer user.Finally,the experimental results show that the proposed method can realize the accurate identification of unbalanced load data sets.The identification method for imbalanced load data of specialized variable users based on DeepSMOTE and deep clustering has an accuracy rate as high as 93.2%,which can effectively improve the identification accuracy of imbalanced load samples.