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A new compound wind speed forecasting structure combining multi-kernel LSSVM with two-stage decomposition technique

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Abstract

The aims of this study are to develop a novel compound structure that consists of two-stage decomposition (TSD), hybrid particle swarm optimization gravitational search algorithm (HPSOGSA) and multi-kernel least square support vector machine (MLSSVM) for improving forecasting accuracy. In the most previous wind speed forecasting studies, only one wind speed signal decomposition method is considered, which is insufficient. To better deal with the wind speed time series, TSD method combining complementary ensemble empirical mode decomposition with adaptive noise with wavelet transform is firstly employed in the proposed model to preprocess the wind speed samples; then, binary-valued particle swarm optimization gravitational search algorithm is exploited as feature selection to identify and eliminate the abnormal noise signal within the input candidate matrix that is determined by partial autocorrelation function. The kernel function and the kernel parameters have great influence on the regression performance of LSSVM. To solve these problems, integrations of radial basis function, polynomial (poly) and linear kernel functions by optimal weighted coefficients are constructed as multi-kernel function for LSSVM, namely MKLSSVM, and the parameter combination is tuned by conventional PSOGSA. The feature selection and parameter optimization are realized by hybrid PSOGSA (HPSOGSA) simultaneously. Finally, comprehensive comparison and analysis are carried out using the historical wind speed data from one wind farm of China to illustrate the excellent forecasting performance of TSD–HPSOGSA–MKLSSVM.

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Acknowledgements

This work was supported by the Key Laboratory of Electric Drive and Control of Anhui Higher Education Institutes, Anhui Polytechnic University (DQKJ202003); the Projects of Science and Technology Commission of Shanghai Municipality (Grant No. 15JC1401900 and No.17511107002); the National Natural Youth Fund Project (Grant No. 61803001); the Open Research Fund of Wanjiang Collaborative Innovation Centerfor High-end Manufacturing Equipment, Anhui Polytechnic University (Grant No. GCKJ2018010); and the Natural youth fund project of Anhui Province (Grant No.1808085QF194).

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Authors and Affiliations

  1. Anhui Key Laboratory of Electric Drive and Control, Anhui Polytechnic University, Wuhu, 241000, China

    Sizhou Sun

  2. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200072, China

    Sizhou Sun, Jingqi Fu, Ang Li & Pinggai Zhang

  3. School of Electrical Engineering, Anhui Polytechnic University, Wuhu, 241000, China

    Sizhou Sun

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  1. Sizhou Sun
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  2. Jingqi Fu
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  3. Ang Li
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Correspondence to Sizhou Sun.

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Conflict of interest

There is no conflict of interest exiting in the submission of this manuscript which has been approved by all authors for publication, and there is no information that might be relevant for the editors and reviewers of soft computing. This study develops a new wind speed forecasting model using two-stage signal decomposition, hybrid PSOGSA algorithm and multi-kernel LSSVM. This article does not contain any studies with human participants performed by any of the authors. This article also does not contain any studies with animals performed by any of the authors. I would like to declare on behalf of my co-authors that the work described is original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part.

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Communicated by V. Loia.

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Appendix

Appendix

All abbreviations are collected, and a brief explanation is given in Table 11.

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Sun, S., Fu, J., Li, A. et al. A new compound wind speed forecasting structure combining multi-kernel LSSVM with two-stage decomposition technique. Soft Comput 25, 1479–1500 (2021). https://doi.org/10.1007/s00500-020-05233-8

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