Temporal Dynamic Network With Learnable Coupled Adjacent Matrix for Wind Forecasting

Abstract

Wind forecasting is a typical and fundamental problem in efficient operation of wind power generation systems. Despite of the developed brilliant techniques, current methods for wind forecasting still rely heavily on numerical weather prediction. To investigate the huge amount of meteorological data and to improve the forecasting ability, this paper proposes a temporal dynamic graph convolutional network with learnable coupled adjacent matrix (TCNet), which takes observations of multiple meteorological elements as predictors for wind forecasting. Specifically, for better illustrating the inner property of wind components, a learnable coupled adjacent matrix (CAM) module is introduced. A spatially stable adjacent matrix is built to model the low-frequency part while a temporally dynamic adjacent matrix is developed to extract the high-frequency one. Casting on this mechanism, the CAM can be embedded to vanilla graph convolution network. Apart from that, a simple but effective temporal weights allocating strategy, named temporal dynamic (TED) module, is proposed to depict the cyclicity. By integrating TED with CAM, TCNet can effectively extract the spatio-temporal feature of wind speed and its correlation with other meteorological elements. Using observed datasets of meteorological elements in Denmark and Netherland, we conduct experiments to validate the performance and efficiency of our proposed model. The results indicate the proposed TCNet outperforms the state-of-the-art Graph Convolutional Networks methods and wind forecasting methods.