The uncertainty estimation of wind power prediction is the basis of wind farm optimal dispatching. Firstly

we proposed a qualitative analysis method of sensitive meteorological factors of wind power prediction

and quantitatively evaluated the prediction error distribution of sensitive factors using non-parametric regression and Gaussian fitting respectively. Then

we adopted Monte-Carlo random sampling to realize the uncertainty estimation of the wind farm power prediction. Finally

we verified the validity of the proposed algorithm by case study. The results show that there are significant differences between uncertainties of the forecast error corresponding to different forecast time

the uncertainty estimation of predicted power can be achieved through the Monte-Carlo method

and hourly sample estimation is superior to the overall sample estimation at the same confidence level. Compared with traditional uncertainty estimation methods of wind power forecasting

the proposed method has a low requirement for operating data

and does not need additional model.