为了提高风力发电功率预测的准确性，建立了基于CEEMDAN分解的SMA算法优化LSSVM的短期风电功率组合预测模型。首先，采用完全集合经验模态分解（CEEMDAN）对原始风电功率数据进行分解与重构。随后，为了进一步优化最小二乘向量支持机模型（LSSVM）的参数，引入了黏菌算法（SMA）优化，通过调整惩罚参数和核参数来提高模型性能，最后，构建多种对比模型对比分析表明CEEMDAN-SMA-LSSVM模型预测精度最高，预测结果更接近真实值。研究可用于风电场短期风电功率预测使用。

In order to improve the accuracy of wind power prediction, a short-term wind power combination prediction model based on CEEMDAN decomposition based on SMA optimization LSSVM was established. First, the original wind power sequence was decomposed and reconstructed by using full set empirical mode decomposition. Then, according to the new sequence, the corresponding prediction model was established. In order to optimize the pa-rameters of the least square vector support machine model, the slime mold algorithm was proposed to optimize and improve the model performance by adjusting the parameters of LSSVM. Finally, a variety of comparison models were constructed for comparison and analysis. The results showes that CEEMDAN-SMA-LSSVM have the highest prediction accuracy and the prediction results are closer to the real value. The research can be used to predict the short-term use of wind power in wind farms.