Enhancing Wind Power Forecasts via Bias Correction Technologies for Numerical Weather Prediction Model

With the ongoing energy transition and the increasing installation capacity of wind power generation, recent advancements in research have demonstrated that accurate numerical weather prediction (NWP) can improve the quality of wind power forecasting. While most studies have primarily focused on the effects of bias-corrected NWPs on wind speeds, few studies have explored the relationship between bias-corrected NWPs and wind power forecasts. Therefore, this study aimed to improve wind power forecasts by applying bias correction technologies to NWP-derived wind speeds. Specifically, this study established a judicious post-processing strategy to rectify NWP outputs. Three distinct NWP models—namely, the Radar Weather Research and Forecasting (RWRF), Deterministic Weather Research and Forecasting (WRFD), and a 20-member WRF ensemble prediction system (WEPS)—were systematically subjected to bias correction using decaying average and probability matched mean methodologies to improve wind-speed forecasts. These bias correction techniques are particularly pertinent when dealing with limited NWP datasets. Subsequent to the bias correction of NWP products, hourly and day-ahead wind power forecasts were performed using machine learning or deep learning models. The results indicated that forecasting accuracy was improved after the bias correction of NWP-derived wind speeds. Comparative analyses with experiments conducted without bias correction revealed a reduction in root mean square error (RMSE) for wind speed in RWRF, WRFD, and WEPS during validation months by 46.13%, 5.84%, and 3.82%, respectively, indicating enhanced wind power prediction accuracy. Therefore, the necessity of NWP correction in a typical forecasting process was verified.