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

2,575,785 unique visits
1,023,306 downloads
Since November 1, 2009



Robots online now
Googlebot
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 »


    
 

  3/2017 - 14
View TOC | « Previous Article | Next Article »

 HIGH-IMPACT PAPER 

Wind Speed Prediction with Wavelet Time Series Based on Lorenz Disturbance

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, WANG, P. See more information about  WANG, P. on SCOPUS See more information about  WANG, P. on SCOPUS See more information about WANG, P. on Web of Science, CHENG, P. See more information about  CHENG, P. on SCOPUS See more information about  CHENG, P. on SCOPUS See more information about CHENG, P. on Web of Science, LEI, S. See more information about LEI, S. on SCOPUS See more information about LEI, S. on SCOPUS See more information about LEI, S. 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 (1,200 KB) | Citation | Downloads: 1,219 | Views: 3,963

Author keywords
ARMA model, Lorenz system, renewable energy, wavelet decomposition, wind speed prediction

References keywords
wind(14), energy(10), speed(8), prediction(8), time(6), power(6), systems(5), series(5), forecasting(5), models(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-08-31
Volume 17, Issue 3, Year 2017, On page(s): 107 - 114
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.03014
Web of Science Accession Number: 000410369500014
SCOPUS ID: 85030118150

Abstract
Quick view
Full text preview
Due to the sustainable and pollution-free characteristics, wind energy has been one of the fastest growing renewable energy sources. However, the intermittent and random fluctuation of wind speed presents many challenges for reliable wind power integration and normal operation of wind farm. Accurate wind speed prediction is the key to ensure the safe operation of power system and to develop wind energy resources. Therefore, this paper has presented a wavelet time series wind speed prediction model based on Lorenz disturbance. Therefore, in this paper, combined with the atmospheric dynamical system, a wavelet-time series improved wind speed prediction model based on Lorenz disturbance is proposed and the wind turbines of different climate types in Spain and China are used to simulate the disturbances of Lorenz equations with different initial values. The prediction results show that the improved model can effectively correct the preliminary prediction of wind speed, improving the prediction. In a word, the research work in this paper will be helpful to arrange the electric power dispatching plan and ensure the normal operation of the wind farm.


References | Cited By

Cited-By Clarivate Web of Science

Web of Science® Times Cited: 27 [View]
View record in Web of Science® [View]
View Related Records® [View]

Updated 2 days, 16 hours ago


Cited-By SCOPUS

SCOPUS® Times Cited: 28
View record in SCOPUS®
[Free preview]
View citations in SCOPUS® [Free preview]

Updated 2 days, 16 hours ago

Cited-By CrossRef

[1] Multi-sensors based condition monitoring of rotary machines: An approach of multidimensional time-series analysis, Wang, Teng, Lu, Guoliang, Yan, Peng, Measurement, ISSN 0263-2241, Issue , 2019.
Digital Object Identifier: 10.1016/j.measurement.2018.10.089
[CrossRef]

[2] Wind speed prediction research with EMD-BP based on Lorenz disturbance, Zhang, Yagang, Pan, Guifang, Zhang, Chenhong, Zhao, Yuan, Journal of Electrical Engineering, ISSN 1339-309X, Issue 3, Volume 70, 2019.
Digital Object Identifier: 10.2478/jee-2019-0028
[CrossRef]

[3] A domain association hierarchical decomposition optimization method for cab vibration control of commercial vehicles, He, Shuilong, Tang, Tao, Ye, Mingsong, Xu, Enyong, Deng, Jucai, Tang, Rongjiang, Measurement, ISSN 0263-2241, Issue , 2019.
Digital Object Identifier: 10.1016/j.measurement.2019.02.020
[CrossRef]

[4] A Novel Hybrid Model for Wind Speed Prediction Based on VMD and Neural Network Considering Atmospheric Uncertainties, Zhang, Yagang, Zhao, Yuan, Gao, Shuang, IEEE Access, ISSN 2169-3536, Issue , 2019.
Digital Object Identifier: 10.1109/ACCESS.2019.2915582
[CrossRef]

[5] Wind Speed Prediction of IPSO-BP Neural Network Based on Lorenz Disturbance, Zhang, Yagang, Chen, Bing, Zhao, Yuan, Pan, Guifang, IEEE Access, ISSN 2169-3536, Issue , 2018.
Digital Object Identifier: 10.1109/ACCESS.2018.2869981
[CrossRef]

[6] A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings, Sun, Meidi, Wang, Hui, Liu, Ping, Huang, Shoudao, Fan, Peng, Measurement, ISSN 0263-2241, Issue , 2019.
Digital Object Identifier: 10.1016/j.measurement.2019.06.029
[CrossRef]

[7] 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]

[8] Evaluation of neural network-based methodologies for wind speed forecasting, Samet, Haidar, Reisi, Mohammad, Marzbani, Fatemeh, Computers & Electrical Engineering, ISSN 0045-7906, Issue , 2019.
Digital Object Identifier: 10.1016/j.compeleceng.2019.07.024
[CrossRef]

[9] Short-Term Canyon Wind Speed Prediction Based on CNN—GRU Transfer Learning, Ji, Lipeng, Fu, Chenqi, Ju, Zheng, Shi, Yicheng, Wu, Shun, Tao, Li, Atmosphere, ISSN 2073-4433, Issue 5, Volume 13, 2022.
Digital Object Identifier: 10.3390/atmos13050813
[CrossRef]

[10] Detection of Cracks and damage in wind turbine blades using artificial intelligence-based image analytics, Reddy, Abhishek, Indragandhi, V., Ravi, Logesh, Subramaniyaswamy, V., Measurement, ISSN 0263-2241, Issue , 2019.
Digital Object Identifier: 10.1016/j.measurement.2019.07.051
[CrossRef]

[11] Application and analysis of hydraulic wind power generation technology, Liu, Keyi, Chen, Wei, Chen, Gexin, Dai, Dandan, Ai, Chao, Zhang, Xinwang, Wang, Xin, Energy Strategy Reviews, ISSN 2211-467X, Issue , 2023.
Digital Object Identifier: 10.1016/j.esr.2023.101117
[CrossRef]

[12] An efficient short-term wind speed prediction model based on cross-channel data integration and attention mechanisms, Yu, Enbo, Xu, Guoji, Han, Yan, Li, Yongle, Energy, ISSN 0360-5442, Issue , 2022.
Digital Object Identifier: 10.1016/j.energy.2022.124569
[CrossRef]

[13] Wind speed prediction with RBF neural network based on PCA and ICA, Zhang, Yagang, Zhang, Chenhong, Zhao, Yuan, Gao, Shuang, Journal of Electrical Engineering, ISSN 1339-309X, Issue 2, Volume 69, 2018.
Digital Object Identifier: 10.2478/jee-2018-0018
[CrossRef]

[14] A three-dimensional geometric features-based SCA algorithm for compound faults diagnosis, Hao, Yansong, Song, Liuyang, Cui, Lingli, Wang, Huaqing, Measurement, ISSN 0263-2241, Issue , 2019.
Digital Object Identifier: 10.1016/j.measurement.2018.10.098
[CrossRef]

[15] A novel hybrid model based on VMD-WT and PCA-BP-RBF neural network for short-term wind speed forecasting, Zhang, Yagang, Chen, Bing, Pan, Guifang, Zhao, Yuan, Energy Conversion and Management, ISSN 0196-8904, Issue , 2019.
Digital Object Identifier: 10.1016/j.enconman.2019.05.005
[CrossRef]

[16] A Validity Index for Fuzzy Clustering Based on Bipartite Modularity, Liu, Yongli, Zhang, Xiaoyang, Chen, Jingli, Chao, Hao, Journal of Electrical and Computer Engineering, ISSN 2090-0147, Issue , 2019.
Digital Object Identifier: 10.1155/2019/2719617
[CrossRef]

[17] A Method Based on Lorenz Disturbance and Variational Mode Decomposition for Wind Speed Prediction, ZHANG, Y., GAO, S., BAN, M., SUN, Y., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 2, Volume 19, 2019.
Digital Object Identifier: 10.4316/AECE.2019.02001
[CrossRef] [Full text]

[18] Automatic detection of a wheelset bearing fault using a multi-level empirical wavelet transform, Ding, Jianming, Ding, Chengcheng, Measurement, ISSN 0263-2241, Issue , 2019.
Digital Object Identifier: 10.1016/j.measurement.2018.10.064
[CrossRef]

[19] A novel improved full vector spectrum algorithm and its application in multi-sensor data fusion for hydraulic pumps, Yu, He, Li, Hongru, Li, Yaolong, Li, Yifan, Measurement, ISSN 0263-2241, Issue , 2019.
Digital Object Identifier: 10.1016/j.measurement.2018.10.011
[CrossRef]

[20] Bearing fault diagnosis based on Cluster-contraction Stage-wise Orthogonal-Matching-Pursuit, Song, Liu, Yan, Ruqiang, Measurement, ISSN 0263-2241, Issue , 2019.
Digital Object Identifier: 10.1016/j.measurement.2019.03.061
[CrossRef]

[21] Wind Speed Prediction Using Wavelet Decomposition Based on Lorenz Disturbance Model, Zhang, Yagang, Zhang, Chenhong, Gao, Shuang, Wang, Penghui, Xie, Fenglin, Cheng, Penglai, Lei, Shuang, IETE Journal of Research, ISSN 0377-2063, Issue 5, Volume 66, 2020.
Digital Object Identifier: 10.1080/03772063.2018.1512384
[CrossRef]

[22] A new laboratory test method for tire-pavement noise, Ren, Wanyan, Han, Sen, Fwa, Tien Fang, Zhang, Jiahao, He, Zhihao, Measurement, ISSN 0263-2241, Issue , 2019.
Digital Object Identifier: 10.1016/j.measurement.2019.05.096
[CrossRef]

[23] A Rolling Bearing Fault Diagnosis-Optimized Scale-Space Representation for the Empirical Wavelet Transform, Liu, Zechao, Ding, Jianming, Lin, Jianhui, Huang, Yan, Shock and Vibration, ISSN 1070-9622, Issue , 2018.
Digital Object Identifier: 10.1155/2018/2749689
[CrossRef]

[24] States prediction for solar power and wind speed using BBA‐SVM, Li, Zhen‐Long, Xia, Jing, Liu, An, Li, Peng, IET Renewable Power Generation, ISSN 1752-1416, Issue 7, Volume 13, 2019.
Digital Object Identifier: 10.1049/iet-rpg.2018.5673
[CrossRef]

[25] Wind Speed Interval Prediction Based on Lorenz Disturbance Distribution, Zhang, Yagang, Zhao, Yuan, Pan, Guifang, Zhang, Jinfang, IEEE Transactions on Sustainable Energy, ISSN 1949-3029, Issue 2, Volume 11, 2020.
Digital Object Identifier: 10.1109/TSTE.2019.2907699
[CrossRef]

[26] Short-term wind speed prediction model of VMD-FOAGRNN based on Lorenz disturbance, Pan, Guifang, Han, Jingyi, Zhang, Yagang, 2019 IEEE Sustainable Power and Energy Conference (iSPEC), ISBN 978-1-7281-4930-1, 2019.
Digital Object Identifier: 10.1109/iSPEC48194.2019.8974883
[CrossRef]

Updated 2 days, 16 hours ago

Disclaimer: All information displayed above was retrieved by using remote connections to respective databases. For the best user experience, we update all data by using background processes, and use caches in order to reduce the load on the servers we retrieve the information from. As we have no control on the availability of the database servers and sometimes the Internet connectivity may be affected, we do not guarantee the information is correct or complete. For the most accurate data, please always consult the database sites directly. Some external links require authentication or an institutional subscription.

Web of Science® is a registered trademark of Clarivate Analytics, Scopus® is a registered trademark of Elsevier B.V., other product names, company names, brand names, trademarks and logos are the property of their respective owners.


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