In recent years

the global installed capacity of wind power has grown rapidly

making the enhancement of wind power prediction accuracy crucial for facilitating the integration and consumption of renewable energy. Current research on ultra-short-term wind power prediction often overlooks load characteristics

resulting in an inability to adequately address grid connection requirements and load dispatching demands across different time periods. To address this limitation

this study proposes a novel approach to ultra-short-term wind power prediction error correction that incorporates load peak-valley characteristics. The methodology involves three key steps: first

deriving interannual prediction error characteristics from ultra short-term prediction results of wind farm clusters; second

establishing error correction intervals for load peak and valley periods

calculating corresponding correction coefficients

and analyzing the impact of varying correction radii on the final results; third

validating the proposed method through empirical analysis of wind farm clusters in three northeastern provinces. The results demonstrate that this approach not only improves wind power prediction accuracy but also significantly reduces the occurrence of harmful error days

thereby better meeting the operational requirements of power system dispatch.