Aiming at the problems of univariate processing method of wind power and insufficient fitting ability of prediction model, a combined short-term wind power prediction method based on multivariate phase space reconstruction (MPSR) and whale optimization algorithm optimized deep extreme learning machine (WOA-DELM) is proposed in this paper. Firstly, the meteorological factors associated with wind power are screened out using Pearson correlation coefficients and formed into multivariate time series with wind power series; secondly, the optimal embedding dimension and time delay of each time series are determined using C-C method to achieve multivariate phase space; then, the dataset established by multivariate phase space reconstruction is input into the DELM model, while WOA is used to optimize the weight parameters of DELM to obtain the WOA-DELM prediction model, which is used to predict the short-term wind power and finally obtain the prediction results. The mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) are used as evaluation indicators, combined with example analysis and compared with the traditional model. The results show that the three evaluation indicators obtained by the proposed prediction model are 0.412 0 MW, 0.492 1 MW and 1.782 2%, respectively, which are better than other models and have better stability and prediction performance.