Research on Ultra-Short-Term Prediction Model of Wind Power Based on Attention Mechanism and CNN-BiGRU Combined - Meng, Yuyu; Chang, Chen; Huo, Jiuyuan; Zhang, Yaonan; Mohammed Al-Neshmi, Hamzah Murad; Xu, Jihao; Xie, Tian (Frontiers in Energy Research, 2022)
With the rapid development of new energy technology and the proposal of a ‘DOUBLE CARBON’ goal, the proportion of wind energy and other new energy power generation in the whole power industry continues to increase and occupy a more important position. However, the instability of wind power output brings serious challenges to the safe and stable power grid operation. Therefore, accurate ultra-short-term prediction of wind power is of great significance to the safe and stable power system operation. This paper presents an ACNN-BiGRU wind power ultra-short-term prediction model based on the Attention mechanism and the fusion of convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU). The model takes a single wind turbine as the prediction unit, uses the real-time meteorological data in the wind farm, the historical power data of the wind turbine, and the real-time operation data for parallel training. Then, extracts the key features of the input data through CNN and uses BiGRU network to conduct bidirectional modeling learning on the dynamic changes of the features proposed by CNN. In addition, the Attention mechanism is introduced to give different weights to BiGRU implicit states through mapping weighting and learning parameter matrix so as to complete the ultra-short-term wind power prediction. Finally, the actual observation data of a wind farm in Northwest China are used to verify the feasibility and effectiveness of the proposed model. The model provides a new idea and method for ultra-short-term high-precision prediction of wind power.