Open AccessMissing data filling method based on linear interpolation and lightgbm
Author(s) -
Guilin Huang
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1754/1/012187
Subject(s) - missing data , interpolation (computer graphics) , computer science , data mining , boosting (machine learning) , process (computing) , linear interpolation , context (archaeology) , algorithm , artificial intelligence , pattern recognition (psychology) , machine learning , motion (physics) , paleontology , biology , operating system
In the context of the rapid development of integrated energy and the digital transformation of power grids, data is playing an increasingly important role in the safe operation of power grids. To deepen the value of data application and ensure the accuracy of data application, this paper proposes a data filling method that combines linear interpolation and LightGBM (Light Gradient Boosting Machine) in response to the missing phenomenon in the source network data collection process. The process can generally be divided into 2 steps: First, linear interpolation is exploited to process short-term missing data. Then the LightGBM can be used to process long-term missing data. In the process of using LightGBM, linear interpolation is used to interpolate the independent variables of the input model. Through the above process, the data for the missing ratio could be obtained, which can then be used to complete all data filling in order from high to low. Through actual data test, this method has better data filling performance.