Design of gas concentration prediction model based on EMD-E-LSTM model
For the purpose of predicting the change trend of dissolved gas concentration in transformer oil,and then completing important work such as detection and maintenance.In this paper,the empirical mode decomposition EMD method,Encoder module and the LSTM neural network are combined.A novel EMD-E-LSTM network prediction model is proposed to predict dissolved gas concentration in oil.The prediction results of dissolved C2H6 gas concentration in 110 kV transformer oil show that compared to the E-LSTM prediction method and the EMD-LSTM prediction method,the MAPE of the proposed EMD-E-LSTM network prediction results have decreased by 22.23%and 5.50%,and the root-mean-square error has decreased by 18.18%and 44.02%,while the maximum relative error was between the two.The proposed method can also improve the prediction accuracy of other dissolved gases,showing a good application prospect.
万方数据
维普
