An effective secondary decomposition approach for wind power forecasting using extreme learning machine trained by crisscross optimization. (15th October 2017)

• Record Type: Journal Article
• Authors: Yin, Hao; Dong, Zhen; Chen, Yunlong; Ge, Jiafei; Lai, Loi Lei; Vaccaro, Alfredo; Meng, Anbo
• Is Part Of: Energy conversion and management. Volume 150(2017)
• Page Start: 108
• Page End: 121
• Publication Date: 2017-10-15
• Subjects: Wind power forecasting -- Secondary hybrid decomposition -- Empirical mode decomposition -- Wavelet packet decomposition -- Extreme learning machine -- Crisscross optimization algorithm; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2017.08.014
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: A secondary decomposition approach is applied in the data pre-processing. The empirical mode decomposition is used to decompose the original time series. IMF1 continues to be decomposed by applying wavelet packet decomposition. Crisscross optimization algorithm is applied to train extreme learning machine. The proposed SHD-CSO-ELM outperforms other pervious methods in the literature. Abstract: Large-scale integration of wind energy into electric grid is restricted by its inherent intermittence and volatility. So the increased utilization of wind power necessitates its accurate prediction. The contribution of this study is to develop a new hybrid forecasting model for the short-term wind power prediction by using a secondary hybrid decomposition approach. In the data pre-processing phase, the empirical mode decomposition is used to decompose the original time series into several intrinsic mode functions (IMFs). A unique feature is that the generated IMF1 continues to be decomposed into appropriate and detailed components by applying wavelet packet decomposition. In the training phase, all the transformed sub-series are forecasted with extreme learning machine trained by our recently developed crisscross optimization algorithm (CSO). The final predicted values are obtained from aggregation. The results show that: (a) The performance of empirical mode decomposition can be significantly improved with its IMF1 decomposed by wavelet packet decomposition. (b) The CSO algorithm has satisfactory performance in addressing the premature convergence problem when applied to optimize extreme learning machine. (c) The proposed approach has great advantage over other previous hybrid models in terms of prediction accuracy.