针对风电出力稳定性差、随机性强而导致的模型精度差的问题。提出了一种基于二次分解误差补偿的风电功率短期预测模型。首先建立双向长短期记忆（bidirectional long short-term memory, BiLSTM）预测模型对风电功率进行预测并输出预测误差。其次，采用了一种利用混沌映射初始化种群、引入黄金正弦策略更新滚球蜣螂位置，并添加动态自适应性权重系数来更新偷窃蜣螂的位置的改进蜣螂优化算法（improved dung beetle optimizer, IDBO）对预测模型参数寻优，防止网络陷入局部最优解，自适应搜寻最优参数组合。然后，采用分解-重构-分解的策略，利用自适应噪声的完全集合经验模态分解（complete ensemble empirical mode decomposition with adaptive noise, CEEMDAN）进行首次分解，并且引入样本熵（sample entropy, SE）与K均值（K-means）将序列按频率进行重构并通过变分模态分解（variational mode decomposition, VMD）将高频误差序列分解成不同频段的误差序列，提高后续模型的预测效率及预测精度。最后，将各分量输入误差补偿模型进行预测并引入Attention机制学习不同时间步的特征关系，并给与不同权重值，加强对关键信息的注意力。通过新疆达坂城风电场实测数据验证了所提模型预测精度高，具有显著优势。

Aiming at the problem of poor model accuracy caused by poor stability and strong randomness of wind power output. A short-term prediction model of wind power based on quadratic decomposition error compensation was proposed. Firstly, BiLSTM(bidirectional long short-term memory) prediction model is established to predict wind power and output prediction errors. Secondly, an IDBO (improved dung beetle optimizer) algorithm was used to initialize the population by using chaotic mapping, update the position of rolling dung beetles by introducing golden sine strategy, and update the position of thieving dung beetles by adding dynamic adaptive weight coefficient to optimize the parameters of the prediction model. Prevent the network from falling into the local optimal solution, and adaptively search the optimal parameter combination. Then, using the decomposition-reconstruction-decomposition strategy, CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise) was used for the first decomposition. In addition, SE(sample entropy) and K-means are introduced to reconstruct the sequence according to frequency, and the high-frequency error sequence was decomposed into error sequences of different frequency bands by VMD(variational mode decomposition). Improve the prediction efficiency and accuracy of subsequent models. Finally, the input error compensation model of each component was used to predict and the Attention mechanism was introduced to learn the feature relationship of different time steps and give different weight values to enhance the attention to key information. Through the measured data of a wind farm in Xinjiang, the prediction accuracy of the proposed model is proved to be high and has significant advantages.