Click to open the HelpDesk interface
AECE - Front page banner

Menu:


FACTS & FIGURES

JCR Impact Factor: 0.800
JCR 5-Year IF: 1.000
SCOPUS CiteScore: 2.0
Issues per year: 4
Current issue: Feb 2024
Next issue: May 2024
Avg review time: 54 days
Avg accept to publ: 60 days
APC: 300 EUR


PUBLISHER

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


TRAFFIC STATS

2,575,748 unique visits
1,023,291 downloads
Since November 1, 2009



Robots online now
bingbot
SemanticScholar


SCOPUS CiteScore

SCOPUS CiteScore


SJR SCImago RANK

SCImago Journal & Country Rank




TEXT LINKS

Anycast DNS Hosting
MOST RECENT ISSUES

 Volume 24 (2024)
 
     »   Issue 1 / 2024
 
 
 Volume 23 (2023)
 
     »   Issue 4 / 2023
 
     »   Issue 3 / 2023
 
     »   Issue 2 / 2023
 
     »   Issue 1 / 2023
 
 
 Volume 22 (2022)
 
     »   Issue 4 / 2022
 
     »   Issue 3 / 2022
 
     »   Issue 2 / 2022
 
     »   Issue 1 / 2022
 
 
 Volume 21 (2021)
 
     »   Issue 4 / 2021
 
     »   Issue 3 / 2021
 
     »   Issue 2 / 2021
 
     »   Issue 1 / 2021
 
 
  View all issues  


FEATURED ARTICLE

Analysis of the Hybrid PSO-InC MPPT for Different Partial Shading Conditions, LEOPOLDINO, A. L. M., FREITAS, C. M., MONTEIRO, L. F. C.
Issue 2/2022

AbstractPlus






LATEST NEWS

2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

2021-Jun-30
Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

Read More »


    
 

  2/2019 - 1
View TOC | « Previous Article | Next Article »

 HIGH-IMPACT 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
 
View the paper record and citations in View the paper record and citations in Google Scholar
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: 1,589 | Views: 3,936

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
Quick view
Full text preview
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  «-- Click to see who has cited this paper

[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 125] [SCOPUS Times Cited 152]


[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 107] [SCOPUS Times Cited 116]


[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 32] [SCOPUS Times Cited 40]


[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 201] [SCOPUS Times Cited 232]


[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 50] [SCOPUS Times Cited 49]


[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 618] [SCOPUS Times Cited 737]


[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 9] [SCOPUS Times Cited 11]


[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 116] [SCOPUS Times Cited 145]


[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 54] [SCOPUS Times Cited 63]


[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 223] [SCOPUS Times Cited 255]


[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 65] [SCOPUS Times Cited 80]


[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 53] [SCOPUS Times Cited 59]


[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 92] [SCOPUS Times Cited 108]


[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 155] [SCOPUS Times Cited 184]


[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 107] [SCOPUS Times Cited 131]


[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 134] [SCOPUS Times Cited 150]


[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 63] [SCOPUS Times Cited 168]


[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 126] [SCOPUS Times Cited 149]


[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 101] [SCOPUS Times Cited 114]


[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 7] [SCOPUS Times Cited 11]


[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 56] [SCOPUS Times Cited 60]


[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 54] [SCOPUS Times Cited 74]


[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 48] [SCOPUS Times Cited 55]


[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 54] [SCOPUS Times Cited 60]


[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 106] [SCOPUS Times Cited 121]


[26] E. N. Lorenz, "Nondeterministic theories of climatic change," Quaternary Research, pp.495-506, 1976.
[CrossRef] [Web of Science Times Cited 87] [SCOPUS Times Cited 96]


[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 27] [SCOPUS Times Cited 28]


[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 75] [SCOPUS Times Cited 91]


[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 83] [SCOPUS Times Cited 92]


[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 124] [SCOPUS Times Cited 152]


[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 67] [SCOPUS Times Cited 76]


[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 232] [SCOPUS Times Cited 276]


[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 58] [SCOPUS Times Cited 67]


[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 11] [SCOPUS Times Cited 12]


[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 4206] [SCOPUS Times Cited 5648]


[38] A. Glowacz, W. Glowacz, "Vibration-Based Fault Diagnosis of Commutator Motor," Shock and Vibration, 2018.
[CrossRef] [Web of Science Times Cited 52] [SCOPUS Times Cited 62]


[39] The Sotavento wind farm in Galicia, Spain, 2018. [Online] Available: Temporary on-line reference link removed - see the PDF document



References Weight

Web of Science® Citations for all references: 7,778 TCR
SCOPUS® Citations for all references: 9,924 TCR

Web of Science® Average Citations per reference: 194 ACR
SCOPUS® Average Citations per reference: 248 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 2024-05-17 20:37 in 245 seconds.




Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

Copyright ©2001-2024
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.

Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.

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.




Website loading speed and performance optimization powered by: 


DNS Made Easy