A spatio-temporal forecasting model using optimally weighted graph convolutional network and gated recurrent unit for wind speed of different sites distributed in an offshore wind farm

Accurate wind speed forecasting plays an essential role in scheduling wind power generation. Currently, most existing models predict wind speed just based on temporal features and geographical information of wind turbine sites is usually neglected or not taken full use of, leading to poor prediction performance. To solve this issue, a novel spatio-temporal prediction model based on optimally weighted graph convolutional network (GCN) and gated recurrent unit (GRU) is proposed. First, data preprocessing based on tensor decomposition is applied to recover missing speed values. Second, geographic and dynamic time warping (DTW) distance are both calculated to construct an optimally weighted graph for different turbine sites, which not only takes fully into account the geographic information but also the temporal similarity of speed series. Third, based on the weighted graph, GCN is used to extract spatial features from speed series adequately. Afterwards, by inputting spatial features of each site into GRU, respectively, the spatio-temporal features can be effectively extracted. Finally, model parameters are solved by iteration with a Huber loss. To demonstrate the proposed model, performance indexes including RMSE, MAE, MAPE, R2 and TIC are calculated. Results show that the proposed model has the smallest prediction error of all sites for multi-step short-term forecasting compared with prevalent models. Therefore, the proposed model effectively enhances prediction accuracy by making full use of spatio-temporal features.