Prediction of water wall temperature based on improved grey wolf optimizer and bidirectional long and short term memory network
An improved grey wolf optimizer(MGWO)is used to optimize BiLSTM to predict water wall temperature.The improved algorithm adopts nonlinear factor adjustment strategy,adaptive position update strategy and dynamic weight modification strategy to improve the global optimization ability of the GWO.The improved grey wolf optimizer is used to optimize the number of hidden layers,learning rate and regularization parameters of the BiLSTM model to improve the prediction accuracy of the model.The data of a power plant in Xinjiang are used for prediction simulation.The results show that,the improved optimizer has higher prediction accuracy,and can predict the change trend of wall temperature when the unit is lifting and lowering load.Compared with the LSTM and BiLSTM models,the average root mean square error of the model reduces by 9.86％and 3.69％,respectively,and the overtemperature of water wall temperature can be predicted in advance,which is of great significance for the prevention of overtemperature of water wall.
water wallprediction of wall temperaturebidirectional long and short term memory neural networkimproved grey wolf optimizeradaptive location updates
国家科技期刊平台
NSTL
万方数据
维普
