Wind power prediction method and device based on deep learning fusion model

The invention provides a wind power prediction method and device based on a deep learning fusion model, and the method achieves the prediction of the wind power through the real-time monitoring data of the wind power of a Scada system and the combination of the historical wind power data. Real-time wind power monitoring data of the Scada system and historical wind power data are input into a deep learning fusion model constructed by a convolutional neural network, a BiLSTM network and an Attention attention mechanism to extract text features, and finally the obtained features are merged to obtain fusion features, so that the optimal text features are obtained to efficiently and accurately predict the wind power. Through the method, the scheduling operation plan making accuracy of the power supply system is improved, and the error phenomenon of new energy power generation power prediction can be reduced. 本发明提出一种基于深度学习融合模型的风电功率预测方法及设备，利用Scada系统风电功率实时监测数据及结合历史风电功率数据对风电功率预测，将Scada系统风电功率实时监测数据和历史风电功率数据输入由卷积神经网络、Bi