基于集合经验模态和深浅层学习组合的风电场功率短期预测研究

风电功率的准确预测是减少风电接入电网的不良影响的必要前提。然而风电功率序列在时间上和空间上表现出非平稳性使其难以准确预测，因此提出一种基于集合经验模态分解(EEMD)和深浅层学习组合的短期风电功率组合预测方法，其中深度学习使用稀疏自编码器（SAE）而浅层学习则使用BP神经网络，从而建立EEMD-SAE-BP预测模型。该模型先用EEMD将风电功率原始序列分解为一系列按不同时间尺度分布的分量；然后针对分量中的高频分量建立SAE预测模型，对低频分量则用BP网络建立预测模型；最后将各子序列预测结果叠加得到最终的风电功率预测结果。通过比较几种预测模型的结果，本文提出的预测模型能有效地提高预测精度，有较高的实用价值。

A Hybrid Model for Wind Power Forecasting Based on EEMD and Coupling SAE-BP

In order to reduce the bad effects of wind power when connect to the power grid, it is necessary to predict the wind power accurately. According to the wind power of the non-linear and non-stationary characteristics, a short-term wind power combination forecasting method based on EEMD and Coupling SAE-BP is proposed. In this method, EEMD is used to decompose the wind power series into a series of relatively stable components to reduce the interaction between the characteristic information. Then, the prediction model is established by using SAE to learn the high frequency components while BP predicts the low frequency components. Finally, the prediction results of the wind power are obtained by superimposing the sequence prediction results. The prediction process and the results show that the proposed model can improve the prediction accuracy and have high utilization value.