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Novel forecasting model based on improved wavelet transform, informative feature selection, and hybrid support vector machine on wind power forecasting

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Abstract

Wind speed/power prediction plays an important role in large-scale wind power penetration because of the wind volatility and uncertainty. In this paper, an accurate forecast model is presented based on improved wavelet transform, informative feature selection and hybrid forecast engine. The proposed forecasting engine is based on support vector machine which is an appropriate prediction forecast engine due to its ability to discover natural structures of wind speed/power variation. The mentioned forecast engine is equipped with an intelligent algorithm and enhances its prediction accuracy. For this purpose, we applied a new version of enhanced particle swarm optimization in this work as the optimization algorithm. Effectiveness of the proposed forecast model is extensively evaluated by real-world electricity market through comparison with well-known forecasting methods. Obtained numerical results and analysis demonstrate the validity and superiority of the proposed method.

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Acknowledgements

This research was financially supported by Key Research Institute of Humanities and Social Science at Universities of Henan. The authors thanks to Emate(www.emate.ac.cn) for their excellent language service, as they had supplied translation and native proofreading from native speakers. Meanwhile, the revisions and suggestion on research method provided by IAMSET, an organizer in academic conferences service, are highly appreciated.

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Authors and Affiliations

  1. School of Management, Henan University of Technology, Zhengzhou, Henan, 450001, China

    Zhenling Liu

  2. College of Engineering, Winona State University, Winona, MN, 55987, USA

    Zhenling Liu

  3. Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran

    Mahdi Hajiali

  4. Department of Electrical Engineering, Faculty of Engineering, University of Isfahan, Esfahān, Iran

    Amirhosein Torabi

  5. Department of Electrical Engineering, Istanbul Technical University, Istanbul, Turkey

    Bahman Ahmadi

  6. Northeastern University, Boston, MA, 082715, USA

    Rolando Simoes

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  1. Zhenling Liu
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  2. Mahdi Hajiali
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  3. Amirhosein Torabi
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  4. Bahman Ahmadi
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  5. Rolando Simoes
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Correspondence to Zhenling Liu.

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Liu, Z., Hajiali, M., Torabi, A. et al. Novel forecasting model based on improved wavelet transform, informative feature selection, and hybrid support vector machine on wind power forecasting. J Ambient Intell Human Comput 9, 1919–1931 (2018). https://doi.org/10.1007/s12652-018-0886-0

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