Error-Aware Segmented Hybrid Correction for WRF Wind-Speed Forecasts

In the context of accelerating global climate change and an expedited energy transition, wind power—being a clean and low-carbon renewable resource—has become a key pillar of new-type power systems and plays a crucial role in safeguarding energy security and achieving carbon neutrality. Wind speed is the core determinant of wind-power forecasting; improving wind-speed prediction accuracy is therefore essential for optimizing wind-energy utilization and enhancing wind-farm operational efficiency. Nevertheless, owing to the stochastic and unstable nature of wind speed, precise forecasting remains challenging. This study conducts short-term wind-speed forecasts for a wind farm in Gansu, China, using the Weather Research and Forecasting (WRF) numerical weather prediction model. Within a segmented wind-speed framework, we introduce three complementary correctors—probability density function matching (PDF), gated recurrent unit (GRU), and k-nearest neighbors (KNN)—and thereby propose a Segmented Error-aware Hybrid Correction (SEHC) framework to post-process WRF wind-speed forecasts and evaluate its applicability. Results show that, relative to raw WRF output, SEHC reduces the mean absolute error (MAE) by approximately 80%, markedly contracts box-plot dispersion and tail quantiles, and delivers the most pronounced accuracy gains in the 3–10 m s-1 wind-speed range. In the high-wind tail, SEHC effectively suppresses extreme biases and limit-exceedance risks. Unlike prior global corrections or empirically defined segmentation strategies, SEHC performs error-diagnosis-driven, targeted within-segment corrections, combining physical interpretability with statistical verifiability. The proposed framework provides technical support for fine-scale wind-speed forecasting and offers valuable guidance for wind-power prediction and power-system dispatching.