Uncertainty Analysis of Wind Power Prediction Based on Quantile Regression

Short-term wind power prediction is an effective way to mitigate the impact of large-scale wind power variability incurring to the electric power system. Given the fluctuation of wind energy is random; uncertainty analysis of wind power prediction is very important for engineering application. A risk assessment index of wind power prediction named PaR (Predict at Risk) was proposed based on quantile regression. And an uncertainty analysis model for wind power prediction was established to provide a possible fluctuation range of predicted wind power at any confidence level. Operation data and predicted power from a wind farm in north China are used as a test case to validate proposed model. The results show that the model can tolerate a wide range of different conditions without hypothesis of the error distribution of wind power prediction and is an effective, practical way to provide uncertainty information.