针对目前对风速序列短期预测中不同组合算法预测精度较差、适应性不强等问题，提出一种基于小波变换的组合预测模型算法，将风速序列经小波变换降低波动性与无序性，利用混合蛙跳算法(shuffled frog leaping algorithm，SFLA)优化逆向传播(back propagation，BP)神经网络的初始权值与阈值，将差分进化(difference evolution，DE)算法用于混合蛙跳算法子种群个体寻优策略，提高个体收敛速度与精度。通过将经小波变换分解得到的高、低频分量分别经组合模型算法进行风速预测与重构，通过实例验证，10、30 min相较60 min预测结果平均绝对百分比误差分别提高33.59%、12.21%，均方根误差分别提高28.77%、8.22%，三者平均预测误差分别为0.037、－0.014、0.011 m/s，与混合蛙跳－BP神经网络算法、BP神经网络算法横向对比，结果表明所提组合预测模型算法预测性能指标最佳。

Aiming at the problems of poor prediction accuracy and poor adaptability of different combination algorithms in the current short-term forecasting of wind speed series, this paper proposes a combination forecasting model based on wavelet transform, which reduces the volatility and disorder of wind speed series through wavelet transform. The shuffled frog leaping algorithm(SFLA) is used to optimize the initial weight and threshold of the back propagation(BP) neural network, and the difference evolution(DE) algorithm is used in the SFLA's subpopulation individual optimization strategy which improves the speed and accuracy of individual convergence. The high and low frequency components decomposed by the wavelet transform are respectively used for wind speed prediction and reconstruction through the combined model algorithm. Compared with the 60 min prediction results, the mean absolute percentage errors of 10 min and 30 min were increased by 33.59% and 12.21% respectively, and the root mean square errors were increased by 28.77% and 8.22% respectively. The average prediction errors of the three are 0.037, -0.014, 0.011 m/s, horizontally compared with the SFLA-BP neural network algorithm and the BP neural network algorithm, the results show that the combination forecasting model of this paper predicts the best performance indicators.