This study proposes a digital shadow framework for wind turbine load estimation that integrates a linearized industrial-grade aeroelastic model with a deep learning–based bias correction (BC) method. To address model mismatches and limited inflow representation, a learning-based bias correction strategy is introduced, where static bias terms are first calibrated via wind-speed-dependent fitting, followed by perturbed correction profiles and parametric simulations to construct a digital shadow dataset. A neural network (NN) is then trained to map operating conditions and bias parameters to load estimation errors, enabling adaptive correction under unseen conditions.</p> <p>The proposed method is validated using field data spanning diverse inflow conditions, achieving a reduction in blade bending moment DEL prediction errors at the 25 % span location from 15–25 % to below 5 %. This demonstrates strong robustness and improved capture of inflow–structure interactions. Overall, the framework provides a scalable pathway to data-driven digital shadows and a foundation for future digital twin applications in real-time load estimation and operational optimization.