To improve the wind speed prediction accuracy of large-scale wind farm clusters and ensure the safe and stable operation of China’s power grid

a short-term wind speed hybrid prediction model for wind farm groups based on particle swarm optimization combined with projection pursuit clustering and NS-L-Transformer was proposed. Firstly

the collected wind speed dataset was processed by the methods of variational mode decomposition

depseudo-component removal and wavelet transform

and the wind speed dataset after filtering out noise interference was obtained. Secondly

considering the spatial correlation characteristics of wind speed among wind farm groups

according to the wind speed fluctuation characteristics

the particle swarm optimization based on projection pursuit clustering algorithm was used to analyze the spatial correlation between wind farm groups. Using the evaluation metrics obtained from the algorithm

an optimal classification of field group correlation was carried out based on their spatial correlations

and the classified high-dimensional wind speed dataset was constructed. Finally

the self-attention mechanism of the Transformer model combined with the gating unit mechanism of the LSTM model captured the local characteristics of the wind speed time series

and the NS-L-Transformer model was proposed to predict the wind speed of the constructed high-dimensional wind speed dataset with local characteristics. The wind speed data of a wind farm group in southeast China was selected for simulation analysis

and the results show that the prediction accuracy of wind speed prediction using the classified high-dimensional dataset is greatly improved compared with that of the single wind speed dataset. Furthermore

compared with the Transformer model

the NS-L-Transformer model exhibits reduced prediction errors

which validates the effectiveness of the hybrid prediction model proposed in this paper.