A novel few-shot learning approach for wind power prediction applying secondary evolutionary generative adversarial network. (15th December 2022) - Meng, Anbo; Chen, Shu; Ou, Zuhong; Xiao, Jianhua; Zhang, Jianfeng; Chen, Shun; Zhang, Zheng; Liang, Ruduo; Zhang, Zhan; Xian, Zikang; Wang, Chenen; Yin, Hao; et al.
Abstract: The accuracy and stability of wind power forecasting are very important for the operation of wind farms. However, for the newly built wind farms without sufficient historical data, it is difficult to make a more accurate prediction. Therefore, it is of great significance to explore a method to improve the wind power prediction accuracy with no sufficient historical data available. In this paper, a novel prediction model is proposed to address the few-shot learning problem of wind power prediction in new-built wind farms based on secondary evolutionary generative adversarial networks (SEGAN) and dual-dimension attention mechanism (DDAM) assisted bidirectional gate recurrent unit (BiGRU). The SEGAN first introduces the secondary evolutionary learning paradigm into learning GAN, aiming to learn the marginal distribution of real data and generate high-quality realistic data to augment the training dataset. In the prediction stage, the DDAM is attempted to obtain a new input matrix with global weight allocation and improve the sensitivity of the BiGRU model to the key information of the input data. The proposed SEGAN-DDAM-BiGRU model is validated on the data from the Galicia Wind Farm in Sotavento and the experimental results show that the proposed model is applicative for short-term prediction of new-built wind farms. Highlights: A secondary evolutionary GAN is proposed to generate high-quality new data. The particle swarm optimization is used in the second evolution. A dual attention mechanism is applied to enhance the model sensitivity. The proposed model has advantage over other benchmark models.