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Ultra-short-term wind speed prediction based on multi-scale predictability analysis

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Abstract

Accurate prediction of ultra-short-term wind speed is important in many applications. Because of the different patterns of wind speed, researchers have indicated that a multi-scale prediction method based on wavelet algorithms could improve forecasting. However, traditional multi-scale methods that directly synthetize results of different-scale components may reduce forecasting accuracy because of error accumulation. In this paper, a multi-scale synthesis strategy that takes into account the predictability of different scales is proposed. As the correlation length of each frequency sub-series is different, prediction models are constructed for each frequency sub-series using different numbers of prediction steps. Finally, based on two wind farms in different regions of China and different basic forecast models, eight experiments are performed using real-world wind speed data. Experimental results show that the proposed multi-scale synthesis method has better performance than traditional multi-scale forecasting methods that use direct synthetizing strategies, indicating its utility in wind engineering applications.

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Acknowledgments

This work was supported by National Program on Key Basic Research Project (973 Program) of China under Grant 2012CB215201.

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Authors and Affiliations

  1. School of Energy Science and Engineering, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, China

    Jie Wan, Guorui Ren, Jinfu Liu & Daren Yu

  2. School of Computer Science and Technology, Tianjin University, Tianjin, 300072, China

    Qinghua Hu

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  1. Jie Wan
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  2. Guorui Ren
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Correspondence to Daren Yu.

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Wan, J., Ren, G., Liu, J. et al. Ultra-short-term wind speed prediction based on multi-scale predictability analysis. Cluster Comput 19, 741–755 (2016). https://doi.org/10.1007/s10586-016-0554-0

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