基于EEMD-GRU网络模型的短期风速预测

为了克服因风速信号固有的震动性、非线性特性引起的预测精度不高的问题, 本文提出了使用集合经验模态分解算法和门控循环单元两种方法相结合的组合模型对风速进行预测. 该模型首先对数据进行归一化处理, 使用孤立森林算法, 剔除异常点, 然后用EEMD (ensemble empirical mode decomposition)方法, 将风速拆分成不同尺度的信号, 消除数据的非平稳性, 将分解得到的相对平稳的分量信号分别送入GRU (gated recurrent unit)模型进行训练, 获得各自的预测结果, 最终风速由所有分量各自预测的结果累加得到. 实验中采用实地采集数据进行实验, 结果证实, EEMD-GRU方法相较于目前主流的EEMD-LSTM、EMD-LSTM等方法, 预测精度有明显提升.

Short-term Wind Speed Prediction Based on EEMD-GRU Network Model

For the low prediction accuracy caused by the inherent vibration and nonlinear characteristics of wind speed signal, a combined model of the ensemble empirical mode decomposition (EEMD) and the gated recurrent unit (GRU) is proposed to predict the wind speed. Firstly, the model normalizes the data and removes outliers by isolated forest. The wind speed is then resolved into signals of different scales by EEMD to obtain stable component signals with the non-stationary data removed. The component signals are trained by the GRU model, from which the predictions are accumulated to obtain the final wind speed. The data collected in the field are applied for the experiment. The results show that the EEMD-GRU method has a significant improvement in the prediction accuracy compared with the dominated EEMD-LSTM and EMD-LSTM methods.