基于 MSTAGNN 模型的可解释海上风电场多风机出力预测

Existing research on the power output prediction of offshore wind farms does not take into account the complex spatio-temporal relationship, and the prediction models are mostly “black boxes”, lacking the interpretable ability. To fully exploit the spatio-temporal correlation and realize the interpretability of the model, this paper proposes a power output prediction model of offshore wind farms based on multiple spatio-temporal attention graph neural network (MSTAGNN). Firstly, a graph topology of the offshore wind farm considering the spatial correlation is proposed, and the spatial attention mechanism is introduced to realize the dynamic change of graph topology. Secondly, the graph convolutional network and temporal gated convolution network are separately used to extract spatial and temporal features. Then, the multi-dimensional and multi-head attention mechanism is introduced into the proposed model to obtain multiple interpretable abilities. Finally, based on the real data of 34 wind turbines in Donghai Bridge wind farm, China, this paper conducts the simulation verification. The results show that, compared with traditional prediction models, the proposed model has higher prediction accuracy, and has reasonable interpretability in multiple dimensions of space, feature and time. © 2023 Automation of Electric Power Systems Press. All rights reserved.