A missing power data filling method based on improved random forest algorithm

Abstract

Missing data filling is a key step in power big data preprocessing, which helps to improve the quality and the utilization of electric power data. Due to the limitations of the traditional methods of filling missing data, an improved random forest filling algorithm is proposed. As a result of the horizontal and vertical directions of the electric power data are based on the characteristics of time series. Therefore, the method of improved random forest filling missing data combines the methods of linear interpolation, matrix combination and matrix transposition to solve the problem of filling large amount of electric power missing data. The filling results show that the improved random forest filling algorithm is applicable to filling electric power data in various missing forms. What's more, the accuracy of the filling results is high and the stability of the model is strong, which is beneficial in improving the quality of electric power data.

Keywords

• big data cleaning
• missing data filling
• data preprocessing
• random forest
• data quality