Prediction of Boiler Control Parameters Based on LSTM Neural Network
- Resource Type
- Conference
- Authors
- Yuxin, Hu; Chengke, Guo; Ning, Mei; Ji, Zhang; Zhaokun, Gong; Jian, Zhao
- Source
- 2022 4th Asia Energy and Electrical Engineering Symposium (AEEES) Energy and Electrical Engineering Symposium (AEEES), 2022 4th Asia. :451-457 Mar, 2022
- Subject
- Power, Energy and Industry Applications
Fans
Microwave integrated circuits
Thermal variables control
Neural networks
Coal
Predictive models
Boilers
coal fired boiler
LSTM
maximum information coefficient
stepwise prediction model
time delay estimation
- Language
A prediction method of main control parameters of boiler based on long short-term memory neural network (LSTM) model was proposed in this paper. The maximum information coefficient (MIC) is used to analyze the nonlinear correlation variables in the boiler, and the optimal input feature set is determined with the prediction accuracy as the goal. Combined with engineering practice, multiple LSTM step-by-step prediction models are established between input and output variables. Based on the maximum information coefficient, the time delay estimates between output parameters of each algorithm are obtained. The method is verified by the data of the coal water slurry boiler in a thermal power company. The results show that the method has high prediction accuracy and can meet the needs of boiler parameter prediction.