Open AccessResearch on Main Steam Temperature Prediction Model Based on Improved LSTM Algorithm
Author(s) -
Ling Zheng,
Xin Ye,
Fei Chen
Publication year - 2020
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 - 10.1088/1742-6596/1631/1/012055
Subject(s) - reliability (semiconductor) , computer science , construct (python library) , algorithm , power (physics) , mean squared prediction error , artificial intelligence , thermodynamics , physics , programming language
In order to improve the reliability and accuracy of the main steam temperature trend prediction, a main steam temperature prediction model based on improved LSTM is proposed. Firstly, uses the grey correlation analysis method to select the important influencing factors. Then, a linear structure is introduced into the LSTM structure to construct a main steam temperature prediction model. Finally, based on the historical operating data of the thermal power unit, a simulation experiment is performed to compare the prediction error of the output of the RNN model, the LSTM model, and the improved LSTM model. The results show that the method has higher prediction accuracy for the main steam temperature. At the same time, compared with other traditional methods, this method has better fitting effect, which can be well applied in practical engineering.