A novel transfer learning approach for wind power prediction based on a serio-parallel deep learning architecture. (1st November 2021) - Yin, Hao; Ou, Zuhong; Fu, Jiajin; Cai, Yongfeng; Chen, Shun; Meng, Anbo
Abstract: Although machine learning methods have been widely applied in the wind power prediction field, they are not suitable for building the prediction model of a new-built wind farm because of no sufficient historical data. In this study, a novel deep transfer learning approach is proposed for addressing the few-shot learning problem in multi-step ahead wind power prediction. In the pre-training stage, several convolutional neural networks (CNNs) in parallel are separately connected to the long short-term memory network (LSTM), thus forming a unique serio-parallel CNNs-LSTM (CL) feature extractor. The CL utilizes the CNNs and LSTM to extract both the meteorological and temporal feature information of the neighboring wind farms for facilitating the prediction modeling of the source wind farm. In the transfer-training stage, a transfer strategy is designed to transfer partial network parameters of a well-trained CL feature extractor to construct the prediction model of the target wind farm. In addition, a personalized-training strategy is implemented by using crisscross optimization (CSO) to retrain the parameters of fully-connected layer. The proposed method is validated on a swarm of wind farms located in China and the experimental results show its obvious superiority over the non-transfer models involved in this study. Highlights: A novel deep transfer learning wind power prediction approach is proposed. A serio-parallel deep learning architecture is proposed as the feature extractor. The source wind farm is identified by the maximum mean discrepancy method. The crisscross optimization is proposed to retrain the fully-connected layer. The proposed method has significant advantage over the non-transfer models.