Multi-scale DBNs regression model and its application in wind speed forecasting

Recently, Deep belief networks(DBNs) have been applied in classification and regression, proved to be superior to general algorithms. But its powerful deep feature extraction ability has not yet been fully played so that a novel algorithm, multi-scale DBNs fusing wavelet transform(WT), is proposed in this paper. Based on the advantages of predicting high frequency components from WT by DBN confirmed, the method realizes the effective combination between WT and DBNs. Through the application of wind speed prediction which is typical time series, the results indicate the new algorithm can enhance the forecasting accuracy of DBNs further. At last, the analyzation of optimization for the multi-scale DBNs algorithm is also been done.