Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method. (August 2016)

• Record Type: Journal Article
• Authors: Wang, Shouxiang; Zhang, Na; Wu, Lei; Wang, Yamin
• Is Part Of: Renewable energy. Volume 94(2016)
• Page Start: 629
• Page End: 636
• Publication Date: 2016-08
• Subjects: EMD -- EEMD -- GA -- BP neural network -- Wind speed forecasting; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2016.03.103
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Wind speed is the major factor that affects the wind generation, and in turn the forecasting accuracy of wind speed is the key to wind power prediction. In this paper, a wind speed forecasting method based on improved empirical mode decomposition (EMD) and GA-BP neural network is proposed. EMD has been applied extensively for analyzing nonlinear stochastic signals. Ensemble empirical mode decomposition (EEMD) is an improved method of EMD, which can effectively handle the mode-mixing problem and decompose the original data into more stationary signals with different frequencies. Each signal is taken as an input data to the GA-BP neural network model. The final forecasted wind speed data is obtained by aggregating the predicted data of individual signals. Cases study of a wind farm in Inner Mongolia, China, shows that the proposed hybrid method is much more accurate than the traditional GA-BP forecasting approach and GA-BP with EMD and wavelet neural network method. By the sensitivity analysis of parameters, it can be seen that appropriate settings on parameters can improve the forecasting result. The simulation with MATLAB shows that the proposed method can improve the forecasting accuracy and computational efficiency, which make it suitable for on-line ultra-short term (10 min) and short term (1 h) wind speed forecasting. Highlights: A wind speed forecasting model based on the improved empirical mode decomposition and GA-BP neural network method is proposed. A detailed comparison between EMD and EEMD is given. The proposed hybrid method is compared with the traditional GA-BP forecasting approach and GA-BP with EMD. The sensitivity analysis of parameters is conducted in order to choose appropriate parameters to improve the result.