VSTCformer for Wind Power Interval Forecasting via Adaptive Variational Mode Decomposition and Spatio-Temporal Cross-Attention

Reliable interval forecasting is essential for risk-aware wind power scheduling, yet the strong nonstationarity and complex spatio-temporal coupling of wind power sequences make probabilistic prediction difficult. This study proposes VSTCformer (Variational-mode-decomposition-enhanced Spatio-Temporal Cross-attention transformer), a wind power interval forecasting framework that integrates adaptive variational mode decomposition (VMD) with spatio-temporal cross-attention and multi-quantile regression. The adaptive VMD module automatically determines the number of modes from the uniformity of center-frequency spacing and decomposes the raw power signal into frequency-aligned intrinsic components, while the undecomposed multivariate sequence is preserved to provide global temporal context. Spatial-guided and temporal-modulated attention realize implicit spatio-temporal coupling during encoding, and explicit fusion is achieved through inter-component interaction before the features are passed to the prediction head. Nine quantiles are jointly estimated to produce the median forecast and prediction intervals at multiple confidence levels. Two public benchmarks with markedly different temporal resolution and volatility are used for evaluation. The results show that VSTCformer preserves competitive point-forecast accuracy while substantially improving interval quality: it produces the narrowest prediction intervals at all evaluated horizons and achieves the best coverage-width trade-off in most settings. The central contribution of the framework is therefore an enhanced and well-calibrated uncertainty quantification capability, confirming that decomposition-enhanced spatio-temporal modeling is an effective route for wind power interval forecasting.