The invention discloses a
wind power forecasting method based on a
genetic algorithm optimization BP neural network, comprising the steps: acquiring forecasting reference data from a
data processing module of a
wind power forecasting
system; establishing a forecasting model of the BP neural network to the reference data, adopting a plurality of
population codes corresponding to different structures of the BP neural network, encoding the weight number and threshold of the neural network by every
population to generate individuals with different lengths, evolving and optimizing every
population by using selection, intersection and variation operations of the
genetic algorithm, and finally judging convergence conditions and selecting optimal individual; then initiating the neural network, further training the network by using
momentum BP
algorithm with variable learning rate till up to convergence, forecasting
wind power by using the network; and finally, repeatedly using a forecasted valve to carry out a plurality of times of forecasting in a circle of forecast for realizing multi-step forecasting with spacing time interval. In the invention, the forecasting precision is improved, the calculation time is decreased, and the stability is enhanced.