Abstract: To address the problem of inaccurate wind power prediction caused by the excessive volatility of wind power data, this paper proposes a generalized regression neural network（GRNN） method based on the optimization of complementary ensemble empirical mode decomposition with adaptive noise（CEEMDAN） and dung beetle optimizer（DBO）. A combination of GRNN and DBO optimization is used for ultra-short-term wind power prediction. First, the original wind power sequence is subjected to time-lag characteristic analysis, and the time series with a strong correlation with the predicted moments is selected for multiplexed time-series modeling. Subsequently, the time series with strong time series are subjected to CEEMDAN decomposition, and a set of intrinsic mode functions（IMFs） and a residual term are obtained. Second, the two sets of the above components are inputted into the GRNN network optimized by the DBO algorithm for the prediction of the components. Subsequently, the prediction components are superimposed to obtain the final prediction result. Example analysis shows that the CEEMDAN-DBO-GRNN prediction model proposed in this paper has higher prediction accuracy, and CEEMDAN can reduce the influence of wind power volatility and randomness on the prediction results. The prediction of the hyperparameter model optimized by the DBO algorithm improves the accuracy of the ultra-short-term wind power prediction to a certain extent.