Research and Application Based on Adaptive Boosting Strategy and Modified CGFPA Algorithm: A Case Study for Wind Speed Forecasting

Jiani Heng, Chen Wang, Xuejing Zhao and Liye Xiao
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Jiani Heng: School of Mathematics and Statistics, Lanzhou University, No. 222, TianShui South Road, ChengGuan District, LanZhou 730000, China
Chen Wang: School of Mathematics and Statistics, Lanzhou University, No. 222, TianShui South Road, ChengGuan District, LanZhou 730000, China
Xuejing Zhao: School of Mathematics and Statistics, Lanzhou University, No. 222, TianShui South Road, ChengGuan District, LanZhou 730000, China
Liye Xiao: School of Physical Electronics, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chenghua District, Chengdu 610000, China

Sustainability, 2016, vol. 8, issue 3, 1-25

Abstract: Wind energy is increasingly considered one of the most promising sustainable energy sources for its characteristics of cleanliness without any pollution. Wind speed forecasting is a vital problem in wind power industry. However, individual forecasting models ignore the significance of data preprocessing and model parameter optimization, which may lead to poor forecasting performance. In this paper, a novel hybrid [k, B t ] -ABBP (back propagation based on adaptive strategy with parameters k and B t ) model was developed based on an adaptive boosting (AB) strategy that integrates several BP (back propagation) neural networks for wind speed forecasting. The fast ensemble empirical mode decomposition technique is initially conducted in the preprocessing stage to reconstruct data, while a novel modified FPA (flower pollination algorithm) incorporating a conjugate gradient (CG) is proposed for searching for the optimal parameters of the [k, B t ] -ABBP mode. The case studies of five wind power stations in Penglai, China are used as illustrative examples for evaluating the effectiveness and efficiency of the developed hybrid forecast strategy. Numerical results show that the developed hybrid model is simple and can satisfactorily approximate the actual wind speed series. Therefore, the developed hybrid model can be an effective tool in mining and analysis for wind power plants.

Keywords: sustainable energy; wind speed forecasting; ABBP mode; CGFPA algorithm; data preprocessing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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