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Open Access

Hybrid Deep Learning Model for Short-Term Wind Speed Forecasting Based on Time Series Decomposition and Gated Recurrent Unit

School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Department of Automatic Control and Systems Engineering, the University of Sheffield, Sheffield, S1 3JD, UK
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Abstract

Accurate wind speed prediction has been becoming an indispensable technology in system security, wind energy utilization, and power grid dispatching in recent years. However, it is an arduous task to predict wind speed due to its variable and random characteristics. For the objective to enhance the performance of forecasting short-term wind speed, this work puts forward a hybrid deep learning model mixing time series decomposition algorithm and gated recurrent unit (GRU). The time series decomposition algorithm combines the following two parts: (1) the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and (2) wavelet packet decomposition (WPD). Firstly, the normalized wind speed time series (WSTS) are handled by CEEMDAN to gain pure fixed-frequency components and a residual signal. The WPD algorithm conducts the second-order decomposition to the first component that contains complex and high frequency signal of raw WSTS. Finally, GRU networks are established for all the relevant components of the signals, and the predicted wind speeds are obtained by superimposing the prediction of each component. Results from two case studies, adopting wind data from laboratory and wind farm, respectively, suggest that the related trend of the WSTS can be separated effectively by the proposed time series decomposition algorithm, and the accuracy of short-time wind speed prediction can be heightened significantly mixing the time series decomposition algorithm and GRU networks.

Keywords

deep learninggated recurrent unit (GRU)complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)short termwavelet packet decompositionwind speed prediction

References

1

T. Hu, W. Wu, Q. Guo, H. Sun, L. Shi, and X. Shen, Very short-term spatial and temporal wind power forecasting: A deep learning approach, CSEE Journal of Power and Energy Systems, vol. 6, no. 2, pp. 434–443, 2020.
Google Scholar
2

Y. Ren, P. N. Suganthan, and N. Srikanth, A novel empirical mode decomposition with support vector regression for wind speed forecasting, IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 8, pp. 1793–1798, 2016.
Crossref Google Scholar
3

Y. Wang, D. Wang, and Y. Tang, Clustered hybrid wind power prediction model based on ARMA, PSO-SVM, and Clustering Methods, IEEE Access, vol. 8, pp. 17071–17079, 2020.
Crossref Google Scholar
4

O. Karakuş, E. E. Kuruoğlu, and M. A. Altınkaya, One-day ahead wind speed/power prediction based on polynomial autoregressive model, IET Renewable Power Generation, vol. 11, no. 11, pp. 1430–1439, 2017.
Crossref Google Scholar
5
H. L. Wei, Boosting wavelet neural networks using evolutionary algorithms for short-term wind speed time series forecasting, presented at the International Work-Conference on Artificial Neural Networks, Gran Canaria, Spain, 2019.https://doi.org/10.1007/978-3-030-20521-8_2
Crossref
6

J. Bedi and D. Toshniwal, Empirical mode decomposition based deep learning for electricity demand forecasting, IEEE Access, vol. 6, pp. 49144–49156, 2018.
Crossref Google Scholar
7

M. Khodayar and J. H. Wang, Spatio-temporal graph deep neural network for short-term wind speed forecasting, IEEE Transactions on Sustainable Energy, vol. 10, no. 2, pp. 670–681, 2019.
Crossref Google Scholar
8

L. Yang, M. He, J. Zhang, and V. Vittal, Support-vector-machine-enhanced Markov model for short-term wind power forecast, IEEE Transactions on Sustainable Energy, vol. 6, no. 3, pp. 791–799, 2015.
Crossref Google Scholar
9

J. Yan, K. Li, E. -W. Bai, J. Deng, and A. M. Foley, Hybrid probabilistic wind power forecasting using temporally local Gaussian process, IEEE Transactions on Sustainable Energy, vol. 7, no. 1, pp. 87–95, 2016.
Crossref Google Scholar
10

X. Li, S. Cao, L. Gao, and L. Wen, A threshold-control generative adversarial network method for intelligent fault diagnosis, Complex System Modeling and Simulation, vol. 1, no. 1, pp. 55–64, 2021.
Crossref Google Scholar
11

M. Khodayar, J. Wang, and M. Manthouri, Interval deep generative neural network for wind speed forecasting, IEEE Transactions on Smart Grid, vol. 10, no. 4, pp. 3974–3989, 2019.
Crossref Google Scholar
12

Q. Zhang, F. Li, F. Long, and Q. Ling, Vehicle emission forecasting based on wavelet transform and long short-term memory network, IEEE Access, vol. 6, pp. 56984–56994, 2018.
Crossref Google Scholar
13
W. Yao, P. Huang, and Z. Jia, Multidimensional LSTM networks to predict wind speed, presented at 37th Chinese Control Conference, Wuhan, China, 2018.https://doi.org/10.23919/ChiCC.2018.8484017
Crossref
14

Y. Long, T. Na, and S. Mukhopadhyay, ReRAM-based processing-in-memory architecture for recurrent neural network acceleration, IEEE Transactions on Very Large Scale Integration(VLSI)Systems, vol. 26, no. 12, pp. 2781–2794, 2018.
Crossref Google Scholar
15

C. Li, G. Tang, X. Xue, A. Saeed, and X. Hu, Short-term wind speed interval prediction based on ensemble GRU model, IEEE Transactions on Sustainable Energy, vol. 11, no. 3, pp. 1370–1380, 2020.
Crossref Google Scholar
16

H. Liu, H. Q. Tian, D. F. Pan, and Y. F. Li, Forecasting models for wind speed using wavelet, wavelet packet, time series and artificial neural networks, Applied Energy, vol. 107, pp. 191–208, 2013.
Crossref Google Scholar
17

H. Liu, C. Chen, H. Q. Tian, and Y. F. Li, A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks, Renewable Energy, vol. 48, pp. 545–556, 2012.
Crossref Google Scholar
18

J. Shi, Z. Ding, W. J. Lee, Y. Yang, Y. Liu, and M. Zhang, Hybrid forecasting model for very-short term wind power forecasting based on grey relational analysis and wind speed distribution features, IEEE Transactions on Smart Grid, vol. 5, no. 1, pp. 521–526, 2014.
Crossref Google Scholar
19

M. Khodayar, O. Kaynak, and M. E. Khodayar, Rough deep neural architecture for short-term wind speed forecasting, IEEE Transactions on Industrial Informatics, vol. 13, no. 6, pp. 2770–2779, 2017.
Crossref Google Scholar
20

F. Riaz, A. Hassan, S. Rehman, I. K. Niazi, and K. Dremstrup, EMD-based temporal and spectral features for the classification of EEG signals using supervised learning, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 1, pp. 28–35, 2016.
Crossref Google Scholar
21

T. Wang, T. Liu, K. Liu, J. Jiang, L. Yu, M. Xue, and Y. Meng, An EMD-based filtering algorithm for the fiber-optic SPR sensor, IEEE Photonics Journal, vol. 8, no. 3, pp. 1–8, 2016.
Crossref Google Scholar
22

H. Liu, X. W. Mi, and Y. F. Li, Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network, Energy Conversion and Management, vol. 156, pp. 498–514, 2018.
Crossref Google Scholar
23
R. Ye, Z. Guo, R. Liu, and J. Liu, Short-term wind speed forecasting method based on wavelet packet decomposition and improved Elman neural network, presented at 2016 International Conference on Probabilistic Methods Applied to Power Systems, Beijing, China, 2016.https://doi.org/10.1109/PMAPS.2016.7764077
Crossref
24

L. Zhao, Y. Jia, and Y. Xie, Robust transcale decentralised estimation fusion for multisensor systems based on wavelet packet decomposition, IET Control Theory and Applications, vol. 8, no. 8, pp. 585–597, 2014.
Crossref Google Scholar
25

L. Han, R. Zhang, X. Wang, A. Bao, and H. Jing, Multi-step wind power forecast based on VMD-LSTM, IET Renewable Power Generation, vol. 13, no. 10, pp. 1690–1700, 2019.
Crossref Google Scholar
26

X. W. Zheng, Y. Y. Tang, and J. T. Zhou, A framework of adaptive multiscale wavelet decomposition for signals on undirected graphs, IEEE Transactions on Signal Processing, vol. 67, no. 7, pp. 1696–1711, 2019.
Crossref Google Scholar
27

A. U. Haque, P. Mandal, J. Meng, A. K. Srivastava, T. -L. Tseng, and T. Senjyu, A novel hybrid approach based on wavelet transform and fuzzy ARTMAP networks for predicting wind farm power production, IEEE Transactions on Industry Applications, vol. 49, no. 5, pp. 2253–2261, 2013.
Crossref Google Scholar
28
X. L. Wang and H. Li, One-month ahead prediction of wind speed and output power based on EMD and LSSVM, in Proc. 2009 International Conference on Energy and Environment Technology, Guilin, China, 2009, pp. 439–442.
29

M. A. Colominas, G. Schlotthauer, and M. E. Torres, Improved complete ensemble EMD: A suitable tool for biomedical signal processing, Biomedical Signal Processing and Control, vol. 14, pp. 19–29, 2014.
Crossref Google Scholar
30

A. Komaty, A. O. Boudraa, J. P. Nolan, and D. Dare, On the behavior of EMD and MEMD in presence of symmetric α-stable noise, IEEE Signal Processing Letters, vol. 22, no. 7, pp. 818–822, 2015.
Crossref Google Scholar
31

A. H. Liao, C. C. Shen, and P. C. Li, Potential contrast improvement in ultrasound pulse inversion imaging using EMD and EEMD, IEEE Transactions on Ultrasonics,Ferroelectrics,and Frequency Control, vol. 57, no. 2, pp. 317–326, 2010.
Crossref Google Scholar
32
X. J. Liu, Z. Q. Mi, P. Li, and H. W. Mei, Study on the multi-step forecasting for wind speed based on EMD, in Proc. International Conference on Sustainable Power Generation and Supply, Nanjing, China, 2009, pp. 1–5.
33

P. Mandal, K. Tank, T. Mondal, C. -H. Chen, and M. J. Deen, Predictive walking-age health analyzer, IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 2, pp. 363–374, 2018.
Crossref Google Scholar
34

R. Abdelkader, A. Kaddour, A. Bendiabdellah, and Z. Derouiche, Rolling bearing fault diagnosis based on an improved denoising method using the complete ensemble empirical mode decomposition and the optimized thresholding operation, IEEE Sensors Journal, vol. 18, no. 17, pp. 7166–7172, 2018.
Crossref Google Scholar
35

A. Humeau-Heurtier, G. Mahé, and P. Abraham, Multi-dimensional complete ensemble empirical mode decomposition with adaptive noise applied to laser speckle contrast images, IEEE Transactions on Medical Imaging, vol. 34, no. 10, pp. 2103–2117, 2015.
Crossref Google Scholar
36

S. Saroha and S. K. Aggarwal, Wind power forecasting using wavelet transforms and neural networks with tapped delay, CSEE Journal of Power and Energy Systems, vol. 4, no. 2, pp. 197–209, 2018.
Crossref Google Scholar
37

H. Liu, H. Q. Tian, X. F. Liang, and Y. F. Li, Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks, Applied Energy, vol. 157, pp. 183–194, 2015.
Crossref Google Scholar
38

W. Li, T. Logenthiran, and W. L. Woo, Multi-GRU prediction system for electricity generation's planning and operation, IET Generation Transmission and Distribution, vol. 13, no. 9, pp. 1630–1637, 2019.
Crossref Google Scholar
39

X. Luo, J. Sun, L. Wang, W. Wang, W. Zhao, J. Wu, J. -H. Wang, and Z. Zhang, Short-term wind speed forecasting via stacked extreme learning machine with generalized correntropy, IEEE Transactions on Industrial Informatics, vol. 14, no. 11, pp. 4963–4971, 2018.
Crossref Google Scholar
40
National Renewable Energy Laboratory, https://www.nrel.gov, 2015.
Complex System Modeling and Simulation
Volume 1 Issue 4,
December 2021
Pages 308-321
DOI: 10.23919/CSMS.2021.0026
Cite this article:
Wang C, Liu Z, Wei H, et al. Hybrid Deep Learning Model for Short-Term Wind Speed Forecasting Based on Time Series Decomposition and Gated Recurrent Unit. Complex System Modeling and Simulation, 2021, 1(4): 308-321. https://doi.org/10.23919/CSMS.2021.0026

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Received: 24 August 2021
Revised: 11 October 2021
Accepted: 16 November 2021
Published: 31 December 2021
© The author(s) 2021

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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