基于SSA-CNN-BiGRU-Attention的超短期风电功率预测模型

Ultra-Short Term Forecasting Model of Wind Power Based on SSA-CNN-BiGRU-Attention

针对风电功率预测精度较低的问题,提出一种融合奇异谱分析(SSA)、卷积神经网络(CNN)、双向门控循环单元(BiGRU)及Attention机制的组合预测模型。为抑制风电功率随机波动特性带来的预测功率曲线滞后性问题,采用SSA方法将原始数据序列分解为一系列相对平稳的子分量,并基于各分量模糊熵(FE)值完成各分解分量的有效重构;构建了CNN-BiGRU-Attention模型并用于各重构分量建模预测,其中,CNN网络用以实现各重构分量高维数据特征的有效提取,BiGRU网络用以完成CNN获取的关键特征向量非线性动态变化规律的有效捕捉,Attention机制的引入用于加强对功率数据关键特征的有效学习;通过叠加基于CNN-BiGRU-Attention模型的各重构分量预测值得到最终预测结果。以新疆哈密地区风电场实际运行采集数据为试验样本进行算例分析,结果表明,所提方法可有效缓解风电功率预测结果滞后现象,预测精度全面优于其他预测方法。

To solve the problem of low forecasting accuracy of wind power,a hybrid forecasting model integrating singular spectrum analysis(SSA),convolutional neural network(CNN),bi-directional gated recurrent unit(BiGRU)and Attention mechanism is proposed.In order to suppress the lag problem of the predicted power curve caused by the random fluctuation of wind power,the SSA method is used to decompose the original data into a series of sub-components with a relatively stable fluctuation characteristics,and the effective reconstruction is realized based on the fuzzy entropy(FE)value of each component.The CNN-BiGRU-Attention model is designed for the forecasting of each reconstructed component.For example,CNN network is used to effectively extract the high-dimensional data features of each reconstructed component,BiGRU network is used to capture the nonlinear dynamic changes of key feature vectors obtained by CNN,and the Attention mechanism is used to highlight and strengthen the effective learning of key features of power data.The final forecasting results are obtained by superimposing the prediction values of each reconstruction component based on the CNN-BiGRU-Attention model.The actual operation data of wind farms in the Hami region of Xinjiang province is used as experimental data,the results show that the proposed method can effectively alleviate the lag phenomenon in wind power forecasting,and the forecasting accuracy is fully ahead of other existed prediction methods.