Research on Missing Value Supplement of Energy System Based on Causal Analysis
In view of the dilemma of sensor data loss or missing due to the surrounding environment in traditional industries,a deep learning method based on causal analysis for multivariate data in energy systems is proposed in the case of unknown data distribution,and the missing value is supplemented by the results.First of all,rebalance the samples,and then a model is built based on LSTM's multivariate model.Caus-al analysis is used to optimize the deep learning optimizer and remove the influence factors that are not expected in the learning process.The pseudo-correlation between the eigenvalue and stable deflection is weakened,and the influence of stable deflection on the eigenvalue is ex-cluded by placebo effect.Finally,the eigenvalue is subtracted from the harmful factor to obtain the value of removing the harmful factor,and then the model is optimized to obtain better results.This method solves the problem of underfitting the head data and overfitting the tail data in the process of machine learning.Experiments on multi-variable energy system data sets show that this method is more accurate in converging the missing value interpolation to the true value.
causal analysisneural networklong tail distributionmissing value interpolation
国家科技期刊平台
NSTL
万方数据
维普
