Short-Term Forecasting of Wind Power Based on Error Traceability and Numerical Weather Prediction Wind Speed Correction

Abstract

Numerical weather prediction (NWP) is crucial in the current short-term wind power forecasting (STWPF) based on data, but it is difficult for STWPF to achieve high accuracy due to the limited accuracy of NWP, which poses a serious challenge to the formulation of forward generation plans. In response to the above issues, this article conducts a traceability analysis of the error of STWPF and proposes a wind power prediction method based on NWP wind speed trend correction. Firstly, the causes of existing errors are analyzed to quantify the impact of NWP on prediction accuracy. Secondly, considering the process correlation between measured and predicted wind speeds, improved complete ensemble EMD with adaptive noise (ICEEMDAN) is used to decompose historical measured wind speeds and NWP wind speeds to construct the most relevant low-frequency trend components. Thirdly, a weighted dual constraint mechanism is proposed to select the most similar historical NWP trend segments to correct NWP wind speed. Finally, the corrected wind speed is used for power prediction and completing STWPF. Through the application of this method to a wind farm in Inner Mongolia Autonomous Region, China, which effectively improves the accuracy of NWP and reduces the average RMSE by 1.39% for power prediction, the effectiveness of this method is verified.