The application discloses a
wind power prediction method and
system based on
frequency domain adaptive super parameter optimization, which comprises the following steps: firstly, collecting historical
wind power and meteorological data; secondly, proposing a brand-new
frequency domain coverage
overlap coefficient as a
fitness function, combining the grey wolf optimization
algorithm to adaptively optimize the super parameter of the
variational mode decomposition, and using the optimized parameter to decompose the power sequence; thirdly, calculating the
sample entropy of each component and reconstructing it into three categories of randomness, fluctuation and trend according to the entropy value to reduce the complexity; then, screening the strongly correlated meteorological features for each component based on the Pearson
correlation coefficient, and inputting them into a prediction model for prediction; finally, superimposing the predicted values of each component to obtain the final power prediction result. The application solves the problem of artificial setting of the VMD super parameter, realizes the balance between the
decomposition quality and the prediction efficiency, and significantly improves the accuracy and practicability of the ultra-short-term
wind power prediction.