Missing Completion Method for Load Data Based on Generative Adversarial Imputation Net

Abstract

Missing load data is a common phenomenon, which prevents these measurements from being used properly in subsequent data analysis. In order to solve the problem of missing data, this paper proposes a load data missing completion method based on the generative adversarial imputation net. Firstly, according to the characteristics of load data and the spatio-temporal relationship, a data matrix was constructed considering the influence of meteorological factors on the variation of load data. Secondly, the mask matrix is used to represent the missing data, the missing data value under the mask matrix is predicted by the generator, and the performance of the generator is evaluated by the discriminator. Finally, in order to verify the effectiveness of the proposed method, load data are used to carry out experiments. Through a series of experiments, it is verified that the completion effect of this paper has obtained good indicators on both RMSE and MAE, and can effectively complete load data with missing rate less than 50%