A Wind Speed Combination Forecasting Method Based on Multifaceted Feature Fusion and Transfer Learning for Centralized Control Center

Abstract

• A novel combined model based on transfer learning is proposed for wind speed forecasting in different data features and scenarios. • MVMD is used to decompose the multivariate series with the intention of considering the internal coupling relationship between wind speed and meteorological series. • TDCN-BiLSTM model is established for alleviating the interference between data, extracting multi-scales characteristics. • The IMOGOA is developed to improve the optimization ability of the algorithm. With the establishment of remote centralized control centers for wind farms, the combined model of multiple deep neural networks used in most wind speed prediction methods can no longer meet the requirements of centralized control centers for efficient and low-cost wind speed prediction in different locations. Thus, a model based on multi-layer feature fusion and transfer learning is proposed in this paper for multi-location wind speed forecasting. Under the idea of "offline training and online prediction", the wind speed characteristics of wind farms at typical locations are learned in the offline stage by fusing multifaceted features through a two-channel dilated convolutional network and a bi-directional long short-term memory network, transferring the well-trained models to any target wind farm to achieve online prediction, and integrating the prediction results of each offline model with an improved multi-objective grasshopper optimization algorithm to further enhance the prediction accuracy. Finally, three comparisons reveal that the proposed model outperforms other baseline models in adaptability and accuracy.