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Sustainability 2016, 8(3), 235; doi:10.3390/su8030235

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

1
School of Mathematics and Statistics, Lanzhou University, No. 222, TianShui South Road, ChengGuan District, LanZhou 730000, China
2
School of Physical Electronics, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chenghua District, Chengdu 610000, China
*
Author to whom correspondence should be addressed.
Academic Editor: João P. S. Catalão
Received: 15 December 2015 / Revised: 30 January 2016 / Accepted: 26 February 2016 / Published: 3 March 2016
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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, Bt] -ABBP (back propagation based on adaptive strategy with parameters k and Bt) 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, Bt] -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. View Full-Text
Keywords: sustainable energy; wind speed forecasting; ABBP mode; CGFPA algorithm; data preprocessing sustainable energy; wind speed forecasting; ABBP mode; CGFPA algorithm; data preprocessing
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Heng, J.; Wang, C.; Zhao, X.; Xiao, L. Research and Application Based on Adaptive Boosting Strategy and Modified CGFPA Algorithm: A Case Study for Wind Speed Forecasting. Sustainability 2016, 8, 235.

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