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

摘要：

准确的风功率预测能为风电能源行业提供可靠的指导和决策依据，然而传统的建模方法主要是将风功率预测问题转换为时序预测问题，忽略了机组间的空间信息，因此，提出一种面向多时间步风功率预测的深度时空网络模型。该模型采用编码器-解码器架构设计，首先，编码器根据历史功率信息建图，并使用图注意力网络（GAT）提取融合风场空间信息的机组特征；其次，使用门控循环单元（GRU）提取输入数据中的时间特性，从而得到关于该机组的风能时间特征；最后，在解码器融合编码器输出的时空特征后，使用样本卷积和交互网络（SCINet）融合不同时间尺度分辨率下的时空特征，输出未来多时间步风功率的预测值。在WindFarm1数据集上的实验结果表明，在预测步数为72时，所提模型的绝对平均误差（MAE）低至42.38，相较于双向门控循环单元（Bi-GRU）的MAE下降了4.25%；所提模型的均方根误差（RMSE）低至42.71，相较于Autoformer的RMSE下降了8.70%。而在WindFarm2数据集上的泛化性实验结果表明，所提模型在不同风场中具备适用性，为未来风功率的准确预测提供了一种新的途径。

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