CViTRNN: A Lightweight Recurrent Network With an Embedded Channel-Aware Vision Transformer for Multidimensional Wind Power Forecasting

Abstract

Accurate wind power forecasting (WPF) is critical to power system planning and operational management, thereby enhancing grid reliability under large-scale wind power integration. However, most existing methods remain confined to a one-dimensional formulation, which inadequately captures turbine-level spatial heterogeneity and consequently limits the joint modeling of positional structure, multivariate interactions, and temporal dynamics. To overcome this limitation, we reformulate WPF as a three-dimensional spatiotemporal forecasting problem by reorganizing each time step into a multi-channel spatial frame according to turbine locations. On this basis, we propose CViTRNN, a multidimensional collaborative learning framework that combines a channel-aware vision transformer with a lightweight recurrent unit to model multivariate interactions and temporal dependencies under turbine-level spatial priors. Furthermore, a two-phase autoregressive workflow is devised to extend forecasting from single-turbine settings to cross-turbine scenarios. Extensive experiments demonstrate the superior accuracy and adaptability of the proposed model, while further confirming the soundness of its design and the interpretability of its forecasting behavior.