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A Method Based on Lorenz Disturbance and Variational Mode Decomposition for Wind Speed PredictionZHANG, Y.   , GAO, S. , BAN, M. , SUN, Y. |
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Author keywords
wind speed prediction, atmospheric dynamics system, Lorenz system, artificial neural network
References keywords
wind(27), speed(22), energy(18), forecasting(16), model(13), prediction(11), neural(11), novel(8), systems(7), renewable(7)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2019-05-31
Volume 19, Issue 2, Year 2019, On page(s): 3 - 12
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.02001
Web of Science Accession Number: 000475806300001
SCOPUS ID: 85066296866
Abstract
Wind power is one of the most promising means of power generation. But the time-varying of wind speed is the most fundamental problem for power generation control system. Therefore, accurate wind speed prediction becomes particularly important. However, traditional wind speed predictions often lack consideration of the influence of atmospheric dynamic system. And few papers have introduced VMD method into the field of wind speed prediction. Thus, combined with four neural networks, this paper develops a wind speed prediction method based on Lorenz system and VMD, obtains LD-VMD-Elman wind speed prediction model. Simulation results show that: 1) As for wind speed prediction, Elman neural network has higher prediction accuracy and smaller error. 2) The models which added Lorenz disturbance can describe the actual physical movement of wind more accurately. 3) VMD can abstract the changing rules of different wind speed frequencies to improve the prediction effect. This paper makes up for the lack of consideration of atmospheric dynamic system. The Lorenz equation is used to describe the atmospheric dynamic system, which provides a new thought for wind speed prediction. The LD-VMD-Elman model significantly improves the accuracy of wind speed prediction and contribute to the power dispatch planning. |
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| [1] E. Ssekulima, M. B. Anwar, A. A. Hinai, M. S. Elmoursi, "Wind speed and solar irradiance forecasting techniques for enhanced renewable energy integration with the grid: a review," IET Renewable Power Generation, vol. 10, no. 7, pp. 885-898, 2016. [CrossRef] [Web of Science Times Cited 105] [SCOPUS Times Cited 124] [2] J. Zhao, Y. L. Guo, X. Xiao, J. Z. Wang, D. Z. Chi, Z. H. Guo, "Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method," Applied Energy, vol. 197, pp. 183-202, 2017. [CrossRef] [Web of Science Times Cited 84] [SCOPUS Times Cited 94] [3] W. Y. Y. Cheng, Y. B. Liu, Y. W. Liu, Y. X. Zhang, W. R. Mahoney, T. T. Warner, "The impact of model physics on numerical wind forecasts," Renewable Energy, vol. 55, pp. 347-356, 2013. [CrossRef] [Web of Science Times Cited 26] [SCOPUS Times Cited 31] [4] J. Zhao, Z. H. Guo, Z. Y. Su, Z. Y. Zhao, X. Xiao, F. Liu, "An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed," Applied Energy, vol. 162, pp. 808-82, 2016. [CrossRef] [Web of Science Times Cited 184] [SCOPUS Times Cited 210] [5] Y. G. Zhang, Y. Zhao, G. F. Pan, J. F. Zhang, "Wind speed interval prediction based on Lorenz disturbance distribution," IEEE Transactions on Sustainable Energy, [CrossRef] [Web of Science Times Cited 39] [SCOPUS Times Cited 40] [6] E. Erdem, J. Shi, "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, vol. 88, pp. 1405-1414, 2011. [CrossRef] [Web of Science Times Cited 555] [SCOPUS Times Cited 651] [7] J. Bessac, E. Mihai Constantinescu, M. Anitescu, "Stochastic simulation of predictive space-time scenarios of wind speed using observations and physical models," Annals of Applied Statistics, vol. 12, no.1, pp. 432-45, 2018. [CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 9] [8] P. Ramasamy, S. S. Chandel, A. K. Yadav, "Wind speed prediction in the mountainous region of India using an artificial neural network model," Renewable Energy, vol. 80, pp. 338-347, 2015. [CrossRef] [Web of Science Times Cited 103] [SCOPUS Times Cited 129] [9] G. Song, Q. Dai, "A novel double deep ELMs ensemble system for time series forecasting," Knowledge-Based Systems, vol. 134, pp. 31-49, 2017. [CrossRef] [Web of Science Times Cited 49] [SCOPUS Times Cited 57] [10] H. Liu, H. Q. Tian, X. F. Liang, Y. F. Li, "Wind speed forecasting approach using secondary composition algorithm and Elman neural networks," Applied Energy, vol. 157, pp. 183-194, 2015. [CrossRef] [Web of Science Times Cited 200] [SCOPUS Times Cited 230] [11] Z. S. Yang, J. Wang, "A hybrid forecasting approach applied in wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Energy, vol. 160, pp. 87-100, 2018. [CrossRef] [Web of Science Times Cited 53] [SCOPUS Times Cited 63] [12] J. Z. Wang, S. H. Xiong, "A hybrid forecasting model based on outlier detection and fuzzy time series - A case study on Hainan wind farm of China," Energy, vol. 76, pp. 526-541, 2014. [CrossRef] [Web of Science Times Cited 47] [SCOPUS Times Cited 53] [13] O. Karakus, E. E. Kuruoglu, M. A. Altinkaya, "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] [Web of Science Times Cited 78] [SCOPUS Times Cited 91] [14] Y. G. Zhang, B. Chen, G. F. Pan, Y. Zhao, "A novel hybrid model based on VMD-WT and PCA-BP-RBF neural network for short-term wind speed forecasting," Energy Conversion and Management, vol. 195, pp. 180-197, 2019. [CrossRef] [Web of Science Times Cited 118] [SCOPUS Times Cited 134] [15] Q. H. Hu, S. G. Zhang, M. Yu, Z. X. Xie, "Short-term wind speed or power forecasting with heteroscedastic support vector regression," IEEE Transactions on Sustainable Energy, vol.7, pp. 241-249, 2016. [CrossRef] [Web of Science Times Cited 88] [SCOPUS Times Cited 109] [16] X. B. Kong, X. J. Liu, R. F. Shi, K. Y. Lee, "Wind speed prediction using reduced support vector machines with feature selection," Neurocomputing, vol. 169, pp. 449-456, Dec. 2015. [CrossRef] [Web of Science Times Cited 122] [SCOPUS Times Cited 138] [17] Y. Ren, P. N. Suganthan, 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] [Web of Science Times Cited 37] [SCOPUS Times Cited 141] [18] C. J. Yu, Y. L. Li, Y. L. Bao, H. J. Tang, G. H. Zhai, "A novel framework for wind speed prediction based on recurrent neural networks and support vector machine," Energy Conversion and Management, vol. 178, pp. 137-145, 2018. [CrossRef] [Web of Science Times Cited 99] [SCOPUS Times Cited 117] [19] P. Jiang, Y. Wang, J. Z. Wang, "Short-term wind speed forecasting using a hybrid model," Energy, vol. 119, pp. 561-577, 2017. [CrossRef] [Web of Science Times Cited 92] [SCOPUS Times Cited 105] [20] Y. G. Zhang, C. H. Zhang, S. Gao, P. H. Wang, F. L. Xie, P. L. Cheng, S. Lei, "Wind speed prediction using wavelet decomposition based on Lorenz disturbance model," IETE Journal of Research. [CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 8] [21] Y. H. Chen, Z. S. He, Z. H. Shang, C. H. Li, L. Li, M. L. Xu, "A novel combined model based on echo state network for multi-step ahead wind speed forecasting: A case study of NREL," Energy Conversion and Management, vol. 179, pp. 13-29, 2019. [CrossRef] [Web of Science Times Cited 47] [SCOPUS Times Cited 49] [22] C. J. Huang, P. H. Kuo, "A Short-term wind speed forecasting model by using artificial neural networks with stochastic optimization for renewable energy systems," Energies, vol. 11, no. 10, 2018. [CrossRef] [Web of Science Times Cited 44] [SCOPUS Times Cited 59] [23] Y. G. Zhang, Y. Zhao, S. Gao, "A novel hybrid model for wind speed prediction based on VMD and neural network considering atmospheric uncertainties," IEEE Access. [CrossRef] [Web of Science Times Cited 40] [SCOPUS Times Cited 44] [24] Y. G. Zhang, P. H. Wang, T. Ni, P. L. Cheng, S. Lei. "Wind power prediction based on LS-SVM model with error correction," Advances in Electrical and Computer Engineering, vol. 17, pp.3-8, Jan. 2017. [CrossRef] [Full Text] [Web of Science Times Cited 49] [SCOPUS Times Cited 52] [25] L. L. Wang, X. Li, Y. L. Bai, "Short -term wind speed prediction using an extreme learning machine model with error correction," Energy Conversion and Management, vol. 162, pp. 239-250, 2018. [CrossRef] [Web of Science Times Cited 91] [SCOPUS Times Cited 106] [26] E. N. Lorenz, "Nondeterministic theories of climatic change," Quaternary Research, pp.495-506, 1976. [CrossRef] [Web of Science Times Cited 83] [SCOPUS Times Cited 91] [27] Y. G. Zhang, P. H. Wang, P. L. Cheng, S. Lei, "Wind speed prediction with wavelet time series based on Lorenz disturbance," Advances in Electrical and Computer Engineering, vol. 17, pp.107-114, 2017. [CrossRef] [Full Text] [Web of Science Times Cited 26] [SCOPUS Times Cited 27] [28] R. Ye, Q. Dai, "A novel transfer learning framework for time series forecasting," Knowledge-Based Systems, vol. 156, pp. 74-99, 2018. [CrossRef] [Web of Science Times Cited 62] [SCOPUS Times Cited 71] [29] J. Naik, S. Dash, P. K. Dash, R. Bisoi, "Short term wind power forecasting using hybrid variational mode decomposition and multi-kernel regularized pseudo inverse neural network," Renewable Energy, vol. 118, pp. 180 -212, 2018. [CrossRef] [Web of Science Times Cited 70] [SCOPUS Times Cited 78] [30] J. Medina, M. Ojeda-Aciego. "Multi-adjoint t-concept lattices," Information Sciences, vol. 180, no. 5, pp. 712-725, 2010. [CrossRef] [Web of Science Times Cited 123] [SCOPUS Times Cited 151] [31] C. Pozna, R. E. Precup, J. K. Tar, I. Skrjanc, S. Preitl, "New results in modelling derived from Bayesian filtering," Knowlege-Based Systems, vol. 23, no. 2, pp. 182-194, 2010. [CrossRef] [Web of Science Times Cited 56] [SCOPUS Times Cited 63] [32] J. Saadat, P. Moallem, H. Koofigar, "Training echo state neural network using harmony search algorithm," International Journal of Artificial Intelligence, vol. 15, no. 1, pp. 163-179, 2017. [33] J. Ruiz-Rangel, C. J. Hernandez, L. M. Gonzalez, D. J. Molinares, "ERNEAD: Training of artificial neural networks based on a genetic algorithm and finite automata theory," International Journal of Artificial Intelligence, vol. 16, no. 1, pp. 214-253, 2018. [34] A. Glowacz, "Acoustic-based fault diagnosis of commutator motor," Mechanical Systems and Signal Processing, vol. 7, no. 11, 2018. [CrossRef] [Web of Science Times Cited 205] [SCOPUS Times Cited 239] [35] A. Glowacz, "Fault diagnosis of single-phase induction motor based on acoustic signals," Electronics, vol. 117, pp. 65-80, 2019. [CrossRef] [Web of Science Times Cited 55] [SCOPUS Times Cited 62] [36] C. H. Yao, Q. Dai, G. Song, "Several Novel Dynamic Ensemble Selection Algorithms for Time Series Prediction," Neural Processing Letters, 2018. [CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 10] [37] K. Dragomiretskiy, D. Zosso, "Variational mode domposition," IEEE Transactions on Signal Processing, vol. 62, pp. 531-544, 2014. [CrossRef] [Web of Science Times Cited 3160] [SCOPUS Times Cited 4206] [38] A. Glowacz, W. Glowacz, "Vibration-Based Fault Diagnosis of Commutator Motor," Shock and Vibration, 2018. [CrossRef] [Web of Science Times Cited 53] [SCOPUS Times Cited 60] [39] The Sotavento wind farm in Galicia, Spain, 2018. [Online] Available: Temporary on-line reference link removed - see the PDF document Web of Science® Citations for all references: 6,266 TCR SCOPUS® Citations for all references: 7,902 TCR Web of Science® Average Citations per reference: 157 ACR SCOPUS® Average Citations per reference: 198 ACR TCR = Total Citations for References / ACR = Average Citations per Reference We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more Citations for references updated on 2023-06-06 21:27 in 208 seconds. Note1: Web of Science® is a registered trademark of Clarivate Analytics. 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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
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Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
