Missing data reconstruction method based on Kmeans and GBDT combined model

Complete and available data is of great significance for improving the theoretical line loss calculation in the low-voltage transformer area. However, with the upgrading of equipment, the electrical data presents the characteristics of small granularity and high complexity, resulting in insufficient line loss calculation accuracy. This paper proposes a missing data reconstruction method based on Kmeans and GBDT combined model. Since it is difficult to unify the reconstruction models of different types of data, the data set is clustered and divided by Kmeans, and GBDT is used for training respectively. During the test, the corresponding GBDT model is used for reconstruction according to the sample category. The simulation results show that the proposed method is suitable for actual data and has higher reconstruction accuracy compared with the traditional mean filling, KNN and decision tree methods. The proposed method can reconstruct the missing of multiple datasets and has good generalization.