A very short term wind power forecasting approach based on numerical weather prediction and error correction method

Abstract

The rapid development of wind power brings great stability challenge to power system due to the randomicity and uncertainty of wind power output. One effective way to overcome the challenge is wind power forecasting. A novel very short-term wind power forecasting approach based on numerical weather prediction and error correction method is presented in the paper. From the statistical analysis of the error probability density distribution of Back Propagation Neural Network (BPNN) and Support Vector Machine (SVM) based wind power prediction, an error estimation model is presented and an error correction method is proposed. The key step of correction method is calculating the corresponding prediction uncertainties according to the predict value. This method combines the advantages of both error estimation model and error probability density distribution, which is evaluated with the data from 7 wind farms and numerical weather prediction, and is proved to be an effective way to improve the accuracy of very short term wind power forecasting.