Under the background of flexible peak regulation

in order to adapt to the dynamic change of direct aircooled unit load and the interference of environmental factors

an online learning neural network method is proposed to predict the backpressure of direct air-cooled unit. Firstly

the historical data are cleaned and Spearman correlation analysis is used to determine the important features of low redundancy affecting backpressure. Then

the Hammerstein model is used to identify the model parameters online for the backpressure. At the same time

the backpressure prediction model of direct air-cooled unit is established by using long-short memory neural network and attention mechanism

and the model is updated by online learning. The experiments results show that

the model has an absolute percentage error(MAPE) of less than 9% in predicting backpressure at different time spans within the next 1 hour

and a MAPE of less than 1% in predicting backpressure within 30 seconds. Finally

the actual power plant system is used to verify that the model can run stably in practical applications. The results of this study provide an effective method for real-time prediction of the backpressure of direct air-cooled unit

which is of great significance for the operation and management of direct air-cooled unit with flexible peak regulation.