The lower upper bound estimation method is an important probabilistic prediction method and has been applied to the solar generation forecasting. However, when the input dimension of the lower upper bound estimation method is large, its performance will be seriously affected. To overcome this challenge, a novel probabilistic prediction of solar generation based on stacked autoencoder and lower upper bound estimation method is proposed. In this method, stacked autoencoder is first used to obtain highly compressed features, which are utilized as the input of the lower upper bound estimation method. Besides, to make the target value in the center of the prediction interval as much as possible, inspired by the idea of support vector machine, the mean squared error of prediction interval is introduced to the loss function, which keeps the target value as far as possible from the lower and upper bounds of the prediction interval. To verify the performance of the proposed method, a large number of experiments have been carried out on the freely available dataset. The results show that the proposed method has better forecasting performance.