基于EMD分解的风功率预测

Wind Power Prediction Based on EMD Decomposition

为了减轻风电波动给风电场电力系统的运行带来的不利影响,提出了一种基于经验模态分解(EMD)的长短时记忆网络(LSTM)算法混合的方法来提高风功率预测的精度。首先,利用EMD方法将风功率时间序列进行分解,提取其中的频域信息和时域信息。再设计LSTM网络的结构和参数,建立风功率预测模型。采用实际风电场运行数据进行预测,实验结果表明,所提出算法的预测误差均小于5%,能够满足实际生产对预测精度的要求。

In order to reduce the adverse effects of wind power fluctuation on the operation of wind farm power system,a hybrid method of long and short time memory network(LSTM)algorithm based on empirical mode decomposition(EMD)is proposed to improve the accuracy of wind power prediction.Firstly,the wind power time series is decomposed by EMD method,and the frequency domain information and time domain information are extracted.Design the structure and parameters of the LSTM network and establish the wind power prediction model.The experimental results show that the prediction errors of the proposed algorithm are less than 5,which can meet the requirements of actual production for prediction accuracy.