As an emerging clean energy, wind energy has become an important part of energy development all over the world. One of the major ways to use wind energy is wind power. Accurate wind power forecasting is significant to the wind energy development and utilization, and the power systems safe and stable operation. Due to the fluctuation and randomness of wind energy, improving the accuracy of ultra-short-term wind energy prediction has become the key to wind energy development and utilization, and it is also the focus of wind energy development research in various countries. Therefore, this paper proposes a new combination model based on complementary empirical mode decomposition (CEEMD), T-S fuzzy neural network (FNN) optimized by improved genetic algorithm (IGA) and Markov error correction to improve the accuracy of ultra-short-term wind power prediction. First, the CEEMD is used to decompose the wind data into several components; then, the trained IGA-FNN model is used to individually predict each modal component to improve accuracy and stability; finally, the prediction results of all modal components are superimposed and the Markov process is used for error correction to obtain the final prediction result. The empirical results show that the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the proposed model is 15.59%, 17.95% and 6.94%, respectively. The empirical result proves that compared with the BPNN, Elman NN, and FNN, the prediction results MAE of the proposed method is reduced by 68 0.6%, 61.7%, 59.2%, the RMSE is reduced by 70.7%, 65.0%, 63.9%, the MAPE is reduced by 75.5%, 67.6%, 60.4%. The prediction accuracy of the proposed method is significantly higher, and it is available for wind power development and utilization.

作为一种新兴的清洁能源，风能已成为全世界能源发展的重要组成部分。利用风能的主要方法之一是风力。准确的风电预测对风能的开发利用和电力系统安全稳定运行具有重要意义。由于风能的波动性和随机性，提高超短期风能预测的准确性已成为风能开发利用的关键，也是各国风能发展研究的重点。因此，本文提出了一种基于互补经验模式分解（CEEMD）的新组合模型，通过改进遗传算法（IGA）和马尔可夫误差校正对TS模糊神经网络（FNN）进行了优化，以提高超短期风电功率预测的准确性。首先，使用CEEMD将风数据分解为几个部分。然后，使用经过训练的IGA-FNN模型分别预测每个模态分量，以提高准确性和稳定性。最后，将所有模态分量的预测结果进行叠加，并使用马尔可夫过程进行纠错，以获得最终的预测结果。实证结果表明，所提出模型的平均绝对误差（MAE），均方根误差（RMSE）和平均绝对百分比误差（MAPE）分别为15.59％，17.95％和6.94％。实证结果证明，与BPNN，Elman NN和FNN相比，该方法的预测结果MAE分别降低了68 0.6％，61.7％，59.2％，RMSE降低了70.7％，65.0％，63.9％，MAPE降低了75.5％，67.6％，60.4％。该方法的预测精度明显更高，可用于风电的开发利用。