基于集合经验模态分解和编码器-解码器的风电功率多步预测

Multi-Step Prediction of Wind Power Based on Ensemble Empirical Mode Decomposition and Encoder-Decoder

准确的风电功率预测对于推动风电大规模并网具有积极意义,现有的研究多集中于超短期范围内的单步预测。为了实现更加贴近工程应用实际的风电功率多步预测,提出了一种基于集合经验模态分解和编码器-解码器的风电功率多步预测方法。首先采用k均值聚类算法对风电机组进行聚类,然后引入集合经验模态分解算法对机组群功率序列进行分解,从而提取风电场功率的时空分布特征,通过预先搭建的基于门控循环单元的编码器-解码器预测网络实现风电功率的超前多步预测,最后将各预测值重构获得风电场总功率的预测值。利用某风电场的真实数据进行算例分析,结果表明所提算法在超前1~6 h不同应用场景下的预测性能均优于其他传统模型,预测准确度提升了6.45%~13.56%。

Accurate prediction of wind power is of great significance to promote large-scale integration of wind power for the grid.Most of the existing researches focus on single-step prediction in the ultra-short-term range.In order to achieve multi-step prediction of wind power which is closer to the engineering application,this paper proposes a multi-step prediction method of wind power based on ensemble empirical mode decomposition and encoder-decoder.First,the k-means cluster algorithm is employed to group wind turbines,and then the ensemble empirical mode decomposition algorithm is used to decompose the power sequence of each group of units.In this way,the temporal and spatial distribution characteristics of wind power are extracted.Advanced multi-step prediction of the wind power is achieved through the encoder-decoder prediction network based on the gated recurrent unit.Finally,by reconstructing the prediction values of all subsequences the total power in a wind farm will be obtained.Taking the data of a real wind farm as example,the simulation results show that the performance of the proposed algorithm is better than other traditional models in different application scenarios such as 1~6 hours ahead,and the prediction accuracy is improved by 6.45%~13.56%.