关键词: 风电机组, 功率预测, 气象因素, 极端天气, 自适应噪声完备集合经验模态分解, 卷积神经网络, 长短期记忆网络, 本征模态函数

Abstract:

To enhance the prediction accuracy of wind turbine output power under diverse meteorological conditions， a power regression prediction method based on complete ensemble empirical mode decomposition with adaptive noise （CEEMDAN）， convolutional neural network（CNN）， and long short-term memory（LSTM） network was proposed. The CEEMDAN algorithm was employed to decompose the original wind power data into intrinsic mode functions（IMFs） and a residual（RES）. Five meteorological factors， including wind speed， were incorporated， and CNN was applied to extract features. LSTM networks were used to perform regression prediction for each subsequence. The prediction results were then superimposed and reconstructed to obtain the final predicted values. Prediction accuracy was evaluated using mean absolute error and root mean square error. Simulation results indicated that the CEEMDAN-CNN-LSTM model significantly outperformed the random forest-LSTM（RF-LSTM） and support vector machine-LSTM（SVM-LSTM） models in prediction accuracy， with notably improved performance and generalization capability under complex meteorological conditions and extreme weather events.

Key words: wind turbine, power prediction, meteorological factors, extreme weather, complete ensemble empirical mode decomposition with adaptive noise, convolutional neural network, long short-term memory network, intrinsic mode function