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

Authors

Li, Jun ; Ma, Liancai

Affiliation

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

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

[1] Wang Q., Martinez-Anido C.B., Wu H.Y., Florita A.R., Hodge B.M., Quantifying the economic and grid reliability impacts of improved wind power prediction, IEEE Transactions on Sustainable Energy, vol. 7, no. 4, pp. 1525–1537 (2016), DOI: 10.1109/TSTE.2016.2560628.
[2] Liu H.Q., Li W.J., Li Y.C., Ultra-short-term wind power prediction based on copula function and bivariate EMD decomposition algorithm, Archives of Electrical Engineering, vol. 69, no. 2, pp. 271–286 (2020), DOI: 10.24425/aee.2020.133025.
[3] Waskowicz B., Statistical analysis and dimensioning of a wind farm energy storage system, Archives of Electrical Engineering, vol. 66, no. 2, pp. 265–277 (2017), DOI: 10.1515/aee-2017-0020.
[4] Cassola F., Burlando M., Wind speed and wind energy forecast through Kalman filtering of numerical weather prediction model output, Applied Energy, vol. 99, no. 6, pp. 154–166 (2012), DOI: 10.1016/j.apenergy.2012.03.054.
[5] Li J., Li M., Prediction of ultra-short-term wind power based on BBO-KELM method, Journal of Renewable and Sustainable Energy, vol. 11, no. 5, 056104 (2019), DOI: 10.1063/1.5113555.
[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.
[9] Ramon G.D., Matheus H.D.M.R., Sinvaldo R.M., A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting, Energy, vol. 216, 119174 (2021), DOI: 10.1016/j.energy.2020.119174.
[10] Yldz C., Akgz H., Korkmaz D., An improved residual-based convolutional neural network for very short-term wind power forecasting, Energy Conversion and Management, vol. 228, no. 1, 113731 (2021), DOI: 10.1016/j.enconman.2020.113731.
[11] Ribeiro G.T., Mariani V.C., Coelho L.D.S., Enhanced ensemble structures using wavelet neural networks applied to short-term load forecasting, Engineering Applications of Artificial Intelligence, vol. 28, no. June, pp. 272–281 (2019), DOI: 10.1016/j.engappai.2019.03.012.
[12] Liu X., Zhou J., Qian H.M., Short-term wind power forecasting by stacked recurrent neural networks with parametric sine activation function, Electric Power Systems Research, vol. 192, 107011 (2021), DOI: 10.1016/j.epsr.2020.107011.
[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.
[14] Gilles J., Empirical wavelet transform, IEEE Transactions on Signal Processing, vol. 61, no. 16, pp. 3999–4010 (2013), DOI: 10.1109/TSP.2013.2265222.
[15] Wang S.X., Zhang N.,Wu L.,Wang Y.M., Wind speed prediction based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method, Renewable Energy, vol. 94, pp. 629–636 (2016), DOI: 10.1016/j.renene.2016.03.103.
[16] Lanckriet G.R.G., Cristianini N., Bartlett P.L., Ghaoui L.E., Jordan M.I., Learning the kernel matrix with semi-definite programming, Journal of Machine learning research, vol. 5, pp. 323–330 (2002).
[17] Gönen M., Alpaydin E., Multiple kernel learning algorithms, Journal of Machine Learning Research, vol. 12, pp. 2211–2268 (2011).
[18] Wu D., Wang B.Y., Precup D., Boulet B., Multiple kernel learning based transfer regression for electric load forecasting, IEEE Transactions on Smart Grid, vol. 11, no. 2, pp. 1183–1192 (2020), DOI: 10.1109/TSG.2019.2933413.

Date

2021.11.30

Type

Article

Identifier

DOI: 10.24425/aee.2021.138262