Research and Application of a Model Selection Forecasting System for Wind Speed and Theoretical Power Generation

Accurate short-term wind speed forecasting is essential for mitigating wind power variability and supporting stable grid operation. This study proposes a model selection forecasting system (MSFS) that dynamically integrates six deep learning models to enhance predictive accuracy and robustness. Using multi-turbine data from a wind farm in northwest China, the framework identifies the optimal model at each time step through iterative evaluation and retrains the selected models to further improve performance. The Kruskal–Wallis test shows that all forecasting models, including MSFS, maintain statistical consistency with the real wind speed distribution at the 95% confidence level. Uncertainty analysis demonstrates that MSFS more reliable forecasting interval. By coupling MSFS-derived wind speed forecasts with turbine-specific power curves, the system enables reliable theoretical power estimation, offering critical reference information for dispatch planning, reserve allocation, and distinguishing resource-driven variability from turbine performance deviations. The slightly conservative yet highly stable forecasting behavior of MSFS reduces overestimation risks and enhances decision reliability. Overall, the proposed MSFS framework provides a robust, interpretable, and operationally valuable solution for short-term wind energy forecasting, with strong potential for wind farm operation and power system management.