A Deep Learning Approach for Wind Power Prediction Based on Stacked Denoising Auto Encoders Optimized by Bat Algorithm

With rapid growth of wind power integration into the modern power system, Wind Power Prediction (WPP) plays an increasingly important role in the planning and operation of electric power system. However, the wind power time series always exhibits nonlinear and non-stationary characteristics, which is still with great challenge to be predicted accurately. To overcome the challenge, a Stacked Denoising Auto Encoders (SDAE) based deep learning approach for WPP is proposed in this paper. Firstly, SDAE with three hidden layers is designed to capture the nonlinear and complex characteristics from the reference data sequence, and the optimal initial connection weights of the deep neural network is obtained by the layer-wise pre-training process. Secondly, the back propagation algorithm is applied to fine-tune the weights of the whole network. To achieve the optimal network architecture, the Bat Algorithm (BA) is adopted to identify the number of neurons of the hidden layers for each denoising auto encoder (DAE). Finally, the proposed method is evaluated by the data from a real wind farm and compared with the Back-propagation Neural Network (BPNN) and the Support Vector Machine (SVM). The results show that SDAE is with the ability to learn the nonlinear and non-stationary characteristics of wind power data and the WPP Root Mean Square Error (RMSE) are reduced by 3.49% and 1.59% in comparison with BPNN and SVM, respectively, which is applicable for practical applications in electric power system.