A Novel Multi-Step Cross-Decomposition Method Based on Wavelet Transform for Wind Power Prediction

Jianxun Lang

doi:10.1051/e3sconf/202125201015

All issues

Volume 252 (2021)

E3S Web Conf., 252 (2021) 01015

References

Open Access

Issue		E3S Web Conf. Volume 252, 2021 2021 International Conference on Power Grid System and Green Energy (PGSGE 2021)


Article Number		01015
Number of page(s)		5
Section		Power Control Technology and Smart Grid Application
DOI		https://doi.org/10.1051/e3sconf/202125201015
Published online		23 April 2021

L. Han, Z. Rongchang, W. Xuesong, J. Huitian, Multi-step wind power forecast based on VMD-. J., LSTIET.Renew.Power.Gener 13, 1690–1700 (2019) [Google Scholar]
S. Zexian, Z. Shenshen, Z. Jingxuan, Short-term wind power forecasting on multiple scales using VMD decomposition, K-Means clustering and LSTM principal computing. J. IEEE.Access 7, 166917–166929 (2019) [Google Scholar]
Y. Zhang, K. Liu, L. Qin, Wind power multi-step interval forecast based on ensemble empirical mode decomposition-sample entropy and optimized extreme learning machine. J. Power.Syst.Technol 40, 2045–2051 (2016) [Google Scholar]
N. Bokdea, A. Feijóob, K. Kulataa, Analysis of differencing and decomposition preprocessing methods for wind speed prediction. J. Appl.Soft.Comput 71, 926–938 (2018) [Google Scholar]
De. Giorgi, S. Campilongo, A. Ficarella, Compari-son between wind power prediction models based on wavelet decomposition with least-squares support vector machine (ls-svm) and artificial neural network (ann). J. Energies 7, 5251–5272 (2014) [Google Scholar]
A. Tascikaraoglu, B. M. Sanandaji, K. Poolla, Exploiting sparsity of interconnections in spatiotemporal wind speed forecasting using wavelet transform. J. Appl. Energy 165, 735–747 (2016) [Google Scholar]

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[1] L. Han, Z. Rongchang, W. Xuesong, J. Huitian, Multi-step wind power forecast based on VMD-. J., LSTIET.Renew.Power.Gener 13, 1690–1700 (2019) [Google Scholar]

[2] S. Zexian, Z. Shenshen, Z. Jingxuan, Short-term wind power forecasting on multiple scales using VMD decomposition, K-Means clustering and LSTM principal computing. J. IEEE.Access 7, 166917–166929 (2019) [Google Scholar]

[3] Y. Zhang, K. Liu, L. Qin, Wind power multi-step interval forecast based on ensemble empirical mode decomposition-sample entropy and optimized extreme learning machine. J. Power.Syst.Technol 40, 2045–2051 (2016) [Google Scholar]

[4] N. Bokdea, A. Feijóob, K. Kulataa, Analysis of differencing and decomposition preprocessing methods for wind speed prediction. J. Appl.Soft.Comput 71, 926–938 (2018) [Google Scholar]

[5] De. Giorgi, S. Campilongo, A. Ficarella, Compari-son between wind power prediction models based on wavelet decomposition with least-squares support vector machine (ls-svm) and artificial neural network (ann). J. Energies 7, 5251–5272 (2014) [Google Scholar]

[6] A. Tascikaraoglu, B. M. Sanandaji, K. Poolla, Exploiting sparsity of interconnections in spatiotemporal wind speed forecasting using wavelet transform. J. Appl. Energy 165, 735–747 (2016) [Google Scholar]