As a new data forecasting model, the double BP neural network combined model based on regressive neural network and time-delay neural network was proposed, which has the advantages of nonlinear combined forecasting methods. The model was trained with main steam flow values of one 660MW unit. The calculation results testified that the double BP neural network model improved the forecasting accuracy of a single model. The mean relative forecasting error of checking samples data was 1.5%, while the mean relative forecasting errors of regression neural network and time-delay neural network were 2.7% and 1.9% respectively. It was proved that the double BP neural network combined model had preferable forecasting accuracy and can be applied for validation of real-time data in power plant.
Proceedings of the CSEE
double BP artificial neural network
regression neural network
time-delay neural network