关键词: 风功率预测, 时空网络, 图注意力网络, 样本卷积和交互网络, 门控循环单元, 时间序列

Abstract:

Reliable guidance and foundation for decision-making in wind power energy industry can be provided by accurate wind power prediction. However， the traditional modeling methods mainly transform wind power prediction problem into a time series prediction problem， ignoring the spatial information among turbines. Therefore， a deep spatio-temporal network model for multi-time step wind power prediction was introduced with an encoder-decoder architecture employed in the model. Firstly， a map was constructed based on historical power information by the encoder， and turbine features integrating spatial information of the wind farm were extracted using Graph ATtention network （GAT）. Secondly， the temporal characteristics of the input data were extracted by Gated Recurrent Unit （GRU）， thereby obtaining the temporal features of wind energy of this turbine. Finally， after fusing the spatio-temporal features output by the encoder in the decoder， Sample Convolution and Interaction Network （SCINet） was used to integrating spatio-temporal features at different time scale resolutions， and prediction for future wind power over multiple time steps were output. Experimental results on WindFarm1 dataset show that with 72 prediction steps， the proposed model has the Mean Absolute Error （MAE） reduced to 42.38， representing a 4.25% improvement over Bidirectional Gated Recurrent Unit （Bi-GRU）； the proposed model has the Root Mean Square Error （RMSE） reduced to 42.71， showing an 8.70% improvement over Autoformer. The results of the generalization experiments on the WindFarm2 dataset demonstrate the proposed model’s applicability to different wind farms， providing a new way to accurately predict future wind power.

Key words: wind power prediction, spatio-temporal network, Graph ATtention network (GAT), Sample Convolution and Interaction Network (SCINet), Gated Recurrent Unit (GRU), time series