Currently, good result is gained in classification problems by making use of deep learning method that also began to be applied in parts of regression tasks. However, more research have focused on forecasting accuracy compared with other regression algorithm and the robust problem of forecasting regression algorithm with practical application demand is ignored. In this paper, a Deep Belief Networks (DBN) model with three hidden layers is constructed by the Restricted Boltzmann Machine (RBM) for multistep regression task. Then three typical multistep regression models are built including support vector regression (SVM), single hidden layers neural networks (SHL-NN) and neural networks with three hidden layers (THL-NN). And day-ahead prediction experiments were carried out with actual wind speed data of wind farm in Ningxia of china. By comparing the experimental results, it’s found that the prediction error, consisting of both mean value and variance, of the DBN model with only three hidden layers is less than those of the other three typical approaches. Consequently, the generation type DBN is not only of high forecast accuracy, but its algorithm robustness is better than the other three algorithms. And the generation type DBN is generally better able to meet the needs of the actual engineering application of wind speed forecasting.