The invention discloses an empirical mode
decomposition and Elman neural network combined
wind power forecasting method, comprising the following steps: screening samples which are used for forecasting a
wind power field, and selecting
wind power outputs of forecasting periods within fluctuation months to implement forecasting; implementing empirical mode
decomposition on multiple groups of output
time sequence sample data of the wind power field, and ensuring that each group can obtain multiple intrinsic mode functions (IMFs) and trend components Res according to
decomposition termination conditions; implementing fluctuation degree classification on decomposed IMFs according to a run distinguishing method, and reconstructing the IMFs according to a similar fluctuation frequency principle to obtain total high-frequency components and total low-frequency components; establishing an Elman neural
network model, and implementing data normalization on the total high-frequency components, the total low-frequency components and the trend components to obtain training and
test data of a neural network; and implementing day-ahead power forecasting for 72h by adopting an Elman improved learning
algorithm to obtain a day-ahead forecasting power value of 72h of target wind power outputs. By adopting the empirical mode decomposition and Elman neural network combined
wind power forecasting method disclosed by the invention, the number of forecasting components can be reduced, and the forecasting accuracy and forecasting speed can be increased.