ABSTRACT With the increasing scale of offshore wind farms, the spatial‐temporal correlation of wind turbines is commonly considered in predicting wind power generation. Meanwhile, the seasonal variation of offshore wind conditions necessitates the consideration of the spatial relationship of wind farms with dynamic changes. This paper proposes a new power prediction model for offshore wind farms, namely the feature attention graph convolutional neural network with temporal transformers (FAGTTN). Specifically, the feature attention module is utilised to extract important features from the offshore wind power supervisory control and data acquisition (SCADA) system data. Then, the adaptive graph convolutional neural network (AGCN) is employed to learn the embedding of multiple wind turbine nodes, uncovering the hidden spatial dependence in the data to express the dynamic spatial relationship of offshore wind farms. Besides, the temporal transformer is used to capture time dependence and temporal patterns in the time series. The proposed method is validated using the real‐world data from the offshore wind farm at Donghai Bridge, demonstrating its validity and superiority. The results show that the proposed offshore wind turbine graph topology network can effectively utilise the geographic location information of wind turbines and outperform existing methods in terms of accuracy and interpretability for offshore wind turbine output prediction.
Su et al. (Thu,) studied this question.