Multi-scale Fusion Prediction Method for Wind Power Cluster Power Based on Spatio-Temporal Graph Spectral Clustering and Time-Series 2D-Reshaping Transformer

[Objective] To address limitations in spatio-temporal feature extraction and the integration of multi-scale features in wind power cluster prediction， this paper proposes a multi-scale fusion prediction method based on spatio-temporal graph spectral clustering （ST-SpecCluster） and a time-series 2D-reshaping Ttransformer （T2Dformer）. [Methods] A spatial skeleton is constructed utilizing the geographical coordinates of wind farms. A hybrid spatio-temporal graph is built to capture both static geographical proximity and dynamic operational correlations by dynamically calculating the Pearson correlation coefficients between wind farms using power data within sliding time windows. Deep spatio-temporal features are extracted via a spatio-temporal graph convolutional network （ST-GCN）， and an attention aggregation mechanism is combined with spectral clustering to partition the wind farms into highly correlated sub-clusters. For each sub-cluster， the power and numerical weather prediction （NWP） sequences are decomposed into trend， periodic， and residual components using the seasonal-trend decomposition procedure based on loess （STL）. A novel prediction model， the T2Dformer， is then designed to jointly model multi-scale features through the collaboration of fast Fourier transform （FFT） period extraction， time-series 2D reshaping， Inception convolution， and attention mechanisms. Each component of every cluster is predicted separately， followed by aggregation and reconstruction. [Results] The proposed method was applied to a wind farm cluster in Jilin Province， China. Compared with state-of-the-art prediction methods， the proposed approach reduced the normalized root mean square error by 1.89%， reduced the normalized mean absolute error by 2.87%， and increased the coefficient of determination （R²） by 10.87%. [Conclusions] This paper provides an effective solution for high-precision wind power prediction， with practical significance for enhancing the dispatching capability of power systems and the consumption of renewable energy.