基于EEMD-CNN-GRU的短期风向预测

为了提高短期风向的预测精度，提出一种基于集合经验模态分解（Ensemble Empirical Mode Decomposition，EEMD）、卷积神经网络（Convolutional Neural Network，CNN）和门控循环单元网络（Gated Recurrent Unit，GRU）的混合模型：EEMD-CNN-GRU.针对风向序列的随机性和不平稳性等特点，先利用EEMD将数据分解成多个分量；再运用CNN的局部连接和权值共享来提取分量中的潜在特征；最后，使用GRU对CNN所提取的潜在特征进一步构建特征，叠加各分量的预测值，得到最终预测结果.实验结果表明：相对于BP神经网络和长短期记忆网络（Long Short-Term Memory，LSTM）等其他模型，本文所提出的预测方法取得了良好的性能.

Short-term wind direction forecast via EEMD-CNN-GRU

To improve the accuracy of short-term wind direction forecasting, a hybrid model, named EEMD-CNN-GRU, is proposed based on Ensemble Empirical Mode Decomposition (EEMD), Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU).The EEMD is used to decompose the data into multiple components to address the randomness and unsteadiness of wind direction series, then the local connection and weight sharing of CNN are employed to extract the potential features in each component, and the GRU is adopted to reconstruct the extracted features and superpose the predicted values of each component to obtain the final prediction results.The experimental results show that the proposed method outperforms models of BP neural network and long short-term memory (LSTM).