Error Correction Method of Ultra-Short-Term Prediction Based on Load Peak-Valley Characteristics for Wind Farm Cluster

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.