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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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 HIGHLY CITED PAPER 

A Method Based on Lorenz Disturbance and Variational Mode Decomposition for Wind Speed Prediction

ZHANG, Y. See more information about ZHANG, Y. on SCOPUS See more information about ZHANG, Y. on IEEExplore See more information about ZHANG, Y. on Web of Science, GAO, S. See more information about  GAO, S. on SCOPUS See more information about  GAO, S. on SCOPUS See more information about GAO, S. on Web of Science, BAN, M. See more information about  BAN, M. on SCOPUS See more information about  BAN, M. on SCOPUS See more information about BAN, M. on Web of Science, SUN, Y. See more information about SUN, Y. on SCOPUS See more information about SUN, Y. on SCOPUS See more information about SUN, Y. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,663 KB) | Citation | Downloads: 623 | Views: 991

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
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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.


References | Cited By

Cited-By ISI Web of Science

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Cited-By CrossRef

SCOPUS® Times Cited: 5
View record in SCOPUS®
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Cited-By CrossRef

[1] A hybrid prediction model for forecasting wind energy resources, Zhang, Yagang, Pan, Guifang, Environmental Science and Pollution Research, ISSN 0944-1344, Issue 16, Volume 27, 2020.
Digital Object Identifier: 10.1007/s11356-020-08452-6
[CrossRef]

[2] Examining the spatiotemporal variations and inequality of China’s provincial CO2 emissions, Wu, Xiaokun, Hu, Fei, Han, Jingyi, Zhang, Yagang, Environmental Science and Pollution Research, ISSN 0944-1344, Issue 14, Volume 27, 2020.
Digital Object Identifier: 10.1007/s11356-020-08181-w
[CrossRef]

[3] A new prediction method based on VMD-PRBF-ARMA-E model considering wind speed characteristic, Zhang, Yagang, Zhao, Yuan, Kong, Chunhui, Chen, Bing, Energy Conversion and Management, ISSN 0196-8904, Issue , 2020.
Digital Object Identifier: 10.1016/j.enconman.2019.112254
[CrossRef]

[4] Short-term wind speed prediction model based on GA-ANN improved by VMD, Zhang, Yagang, Pan, Guifang, Chen, Bing, Han, Jingyi, Zhao, Yuan, Zhang, Chenhong, Renewable Energy, ISSN 0960-1481, Issue , 2020.
Digital Object Identifier: 10.1016/j.renene.2019.12.047
[CrossRef]

[5] Performance Evaluation of a New BP Algorithm for a Modified Artificial Neural Network, Panda, Sashmita, Panda, Ganapati, Neural Processing Letters, ISSN 1370-4621, Issue 2, Volume 51, 2020.
Digital Object Identifier: 10.1007/s11063-019-10172-z
[CrossRef]

[6] Wind Speed Prediction Research Considering Wind Speed Ramp and Residual Distribution, Zhang, Yagang, Gao, Shuang, Han, Jingyi, Ban, Minghui, IEEE Access, ISSN 2169-3536, Issue , 2019.
Digital Object Identifier: 10.1109/ACCESS.2019.2940897
[CrossRef]

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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