Details

Title

Short-term wind power combined prediction based on EWT-SMMKL methods

Journal title

Archives of Electrical Engineering

Yearbook

2021

Volume

vol. 70

Issue

No 4

Affiliation

Li, Jun : Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China ; Ma, Liancai : Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China

Authors

Keywords

combined model ; empirical wavelet transform ; prediction ; soft margin multiple kernel learning ; wind power

Divisions of PAS

Nauki Techniczne

Coverage

801-817

Publisher

Polish Academy of Sciences

Bibliography

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[6] Zhang Y.G., Wang P.H., Zhang C.H., Lei S., Wind energy prediction with LS-SVM based on Lorenz perturbation, The Journal of Engineering, vol. 2017, no. 13, pp.1724–1727 (2017), DOI: 10.1049/joe.2017.0626.
[7] Duan J., Wang P., Ma W., Short-term wind power forecasting using the hybrid model of improved variational mode decomposition and Correntropy Long Short-term memory neural network, Energy, vol. 214, 118980 (2021), DOI: 10.1016/j.energy.2020.118980.
[8] Moreno S.R., Silva R.G. D., Mariani V.C., Multi-step wind speed forecasting based on hybrid multistage decomposition model and long short-term memory neural network, Energy Conversion and Management, vol. 213, 112869 (2020), DOI: 10.1016/j.enconman.2020.112869.
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[13] Zhu R., Liao W., Wang Y., Short-term prediction for wind power based on temporal convolutional network, Energy Reports, vol. 6, pp. 424–429 (2019), DOI: 10.1016/j.egyr.2020.11.219.
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Date

2021.11.30

Type

Article

Identifier

DOI: 10.24425/aee.2021.138262
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