基于GWO-CNN-BILSTM的超短期风电预测

在未来高渗透率风电场景下，超短期风电功率预测研究对于实现电力系统优化运行具有重要意义。为此，提出一种基于GWO-CNN-BiLSTM的超短期风电预测方法。首先，搭建基于卷积神经网络(convolutional neural network, CNN)与双向长短期记忆神经网络(bidirectional long short term memory, BiLSTM)的组合模型，然后，为提升风电预测结果的精度，通过灰狼优化算法(grey wolf optimizer, GWO)对组合模型进行优化，使该组合模型参数能实时适应风电历史数据。最后，仿真结果验证了所提出方法的有效性和优越性。

Ultra-short-term Wind Power Prediction Based on GWO-CNN-BILSTM

In the future high-permeability wind power scenarios, the study of ultra-short-term wind power prediction research is of great significance for achieving optimal operation of power systems. Therefore, an ultra-short-term wind power prediction method based on GWO-CNN-BILSTM is proposed. Firstly, a combined model based on convolutional neural network (CNN) and bidirectional long short term memory (BILSTM) is built, and then, in order to improve the accuracy of wind power prediction results, the combined model is optimized by the grey wolf optimizer (GWO), so that the parameters of the combined model can be adapted to the historical wind power data in real time. Finally, the simulation results verify the effectiveness and superiority of the proposed method.