Dual-channel representation learning with wind speed correction factor for enhanced wind power forecasting

• A correction factor is constructed to describe the prediction error between the measured WS and NWP WS, and to characterize the transfer relationship in the measured WS sequence. • A self-correction method based on correction factor is proposed to correct NWP WS. • CFRM is established to address the problem of short-term scale performance degradation in existing wind speed correction algorithms. • A dual-channel mechanism based on important features is proposed, which upgrades the single-channel architecture of traditional neural networks to a dual-branch structure to enhance the model's ability to learn important features. Wind power forecasting (WPF) accuracy is fundamentally constrained by two critical challenges. First, the high-order nonlinear relationship between wind speed (WS) and power (WP) substantially amplifies errors inherent in numerical weather prediction (NWP) data. Second, conventional models process all input features uniformly, failing to distinguish the dominant role of the primary driving feature from the complementary roles of auxiliary features. To decouple and address these challenges, this paper proposes a novel forecasting method (CFRM-DCM) that integrates a Correction Factor Representation Model (CFRM) and a Dual-Channel Mechanism (DCM). The CFRM is first employed to address the NWP error. It describes the complex correlation and forecasting error between measured WS and NWP WS as correction factors. A generative adversarial network (GAN) is then utilized to learn the distribution of these factors and output a corrected, more accurate WS. This corrected data is then fed into the DCM, a dual-branch architecture designed to enhance complex feature extraction, overcoming the limitations of traditional single-channel structures. The proposed method is validated on four wind farms. Simulation results demonstrate that the CFRM-DCM method achieves significant improvements in WPF accuracy, with error reductions ranging from 3.9% to 9.4% across ultra-short-term and short-term timescales. This enhanced WPF performance is directly attributed to the model's ability to first improve WS accuracy, with gains of 8.8%, 7.6%, 8.3%, and 8.8% for the respective farms.