An improved deep belief network based hybrid forecasting method for wind power

Abstract

The stochastic nature of wind speed hinders the forecasting of wind power generation. To improve the accuracy of wind power forecasting and effectively utilize the capability of principal component analysis (PCA) to process high-dimensional data, and take the advantages of deep belief network (DBN) to process complex data and spatial correlation (SC) in considering geographical position and terrain, a hybrid forecasting method using numerical weather prediction (NWP) is presented in this paper. First, an improved DBN is proposed by introducing Gaussian-Bernoulli restricted Boltzmann machine, and an adaptive learning step technique is applied to improve the convergence speed. Furthermore, the principal components are extracted from high-dimension original data by PCA, which are further input to the improved DBN. Then, a wind speed correction model is established to address the inaccuracy of NWP. Moreover, the output of the target site is forecasted using the data of its neighboring observation sites. Finally, the advantages of the above methods are combined, and the sliding window strategy is utilized to adaptively update the training data. The simulation results verify the effectiveness of the proposed improved DBN and the hybrid method compared to the traditional DBN, the corresponding average increase of forecasting accuracy is 15.8975% and 29.3725%, respectively.