The invention discloses a wind power prediction method based on a self-learning composite data source autoregression model. The wind power prediction method based on the self-learning composite data source autoregression model comprises the steps that data are input to enable parameters of the autoregression model to be obtained; input data required by wind power prediction are input into the autoregression model determined according to the parameters of the autoregression model, so that a prediction result is obtained; post-evaluation is conducted on the prediction result, namely the error between a predicted value and a measured value is analyzed, and order determination of the autoregression model AR(p) and estimation of the parameters of the model AR(p) are conducted again if a predicted error is larger than an allowable maximum error. Key information is provided for new energy power generation real-time scheduling, a new energy power generation day-ahead plan, a new energy power generation monthly plan, new energy power generation capability evaluation and wind curtailment power estimation by predicting the wind power generated during wind power generation. The ultra-short-term wind power prediction accuracy is effectively improved due to the fact a composite data source is introduced, and thus the on-grid energy of new energy resources is effectively increased on the premise that safe, stable and economical operation of a power grid is guaranteed.