Abstract: The uncertainty of wind power prediction results includes data uncertainty and model uncertainty. Firstly, the sources of the two uncertainties are analyzed, and the objectives and forms of wind power probability prediction are given; Secondly, the parameter of the probability distribution of wind power is used as the neural network output, and the negative log likelihood loss as the loss function to model the data uncertainties; Then, the weight parameters of neural network are taken as random variables and expressed by probability distribution to realize the modeling of the model uncertainties; Finally, based on the model, a wind power probability distribution prediction method considering the data uncertainties is proposed. The data of actual wind farms are used for verification, and the prediction performances under different probability distribution assumptions are analyzed. The results show that the method supports three forms of probability prediction: the probability distribution, the interval and the output scenarios. Considering the model uncertainties, the performance of probability distribution prediction is improved. It is also verified that the method can characterize the uncertainty of prediction results under abnormal weather conditions.