A real-time data forecasting model based on modified BP neural network for thermal power plants has been put forward

that means momentum factors and adaptive learning rate have been introduced into standard BP algorithm

so as to reduce the process of convergence oscillation

and to speed up the learning speed. Selecting real - time data of feed-water flow rate of 300 MW unit in one power plant to train the said network for learning and verifying

the influence of nodal point number

learning specimen number

and momentum factors in the input and hidden layers upon forecasting precision of said model has been analysed. Practical examples show that the said model to boast better error - tolerant performance

can satisfy the requirements of performance analysis for thermal power units.