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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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Wind Power Prediction Based on LS-SVM Model with Error Correction

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, NI, T. See more information about  NI, T. on SCOPUS See more information about  NI, T. on SCOPUS See more information about NI, T. 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
 
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 (1,180 KB) | Citation | Downloads: 1,226 | Views: 1,830

Author keywords
computer errors, error correction, support vector machines, power engineering computing, wind energy generation

References keywords
wind(19), energy(15), prediction(11), speed(10), power(7), renewable(6), jrenene(6), term(5), short(5), forecasting(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-02-28
Volume 17, Issue 1, Year 2017, On page(s): 3 - 8
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.01001
Web of Science Accession Number: 000396335900001
SCOPUS ID: 85014266751

Abstract
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As conventional energy sources are non-renewable, the world's major countries are investing heavily in renewable energy research. Wind power represents the development trend of future energy, but the intermittent and volatility of wind energy are the main reasons that leads to the poor accuracy of wind power prediction. However, by analyzing the error level at different time points, it can be found that the errors of adjacent time are often approximately the same, the least square support vector machine (LS-SVM) model with error correction is used to predict the wind power in this paper. According to the simulation of wind power data of two wind farms, the proposed method can effectively improve the prediction accuracy of wind power, and the error distribution is concentrated almost without deviation. The improved method proposed in this paper takes into account the error correction process of the model, which improved the prediction accuracy of the traditional model (RBF, Elman, LS-SVM). Compared with the single LS-SVM prediction model in this paper, the mean absolute error of the proposed method had decreased by 52 percent. The research work in this paper will be helpful to the reasonable arrangement of dispatching operation plan, the normal operation of the wind farm and the large-scale development as well as fully utilization of renewable energy resources.


References | Cited By

Cited-By ISI Web of Science

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Cited-By CrossRef

SCOPUS® Times Cited: 19
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Cited-By CrossRef

[1] Wind Speed Prediction with Wavelet Time Series Based on Lorenz Disturbance, ZHANG, Y., WANG, P., CHENG, P., LEI, S., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 3, Volume 17, 2017.
Digital Object Identifier: 10.4316/AECE.2017.03014
[CrossRef] [Full text]

[2] 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, 2018.
Digital Object Identifier: 10.1080/03772063.2018.1512384
[CrossRef]

[3] Current Status Investigation and Predicting Carbon Dioxide Emission in Latin American Countries by Connectionist Models, Ahmadi, Mohammad Hossein, Dehghani Madvar, Mohammad, Sadeghzadeh, Milad, Rezaei, Mohammad Hossein, Herrera, Manuel, Shamshirband, Shahaboddin, Energies, ISSN 1996-1073, Issue 10, Volume 12, 2019.
Digital Object Identifier: 10.3390/en12101916
[CrossRef]

[4] Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation, Adnan, Rana, Liang, Zhongmin, Yuan, Xiaohui, Kisi, Ozgur, Akhlaq, Muhammad, Li, Binquan, Energies, ISSN 1996-1073, Issue 2, Volume 12, 2019.
Digital Object Identifier: 10.3390/en12020329
[CrossRef]

[5] 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]

[6] Short-term wind power prediction based on data mining technology and improved support vector machine method: A case study in Northwest China, Li, Cunbin, Lin, Shuaishuai, Xu, Fangqiu, Liu, Ding, Liu, Jicheng, Journal of Cleaner Production, ISSN 0959-6526, Issue , 2018.
Digital Object Identifier: 10.1016/j.jclepro.2018.09.143
[CrossRef]

[7] 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]

[8] Wind energy prediction with LS-SVM based on Lorenz perturbation, Zhang, Yagang, Wang, Penghui, Zhang, Chenhong, Lei, Shuang, The Journal of Engineering, ISSN 2051-3305, Issue 13, Volume 2017, 2017.
Digital Object Identifier: 10.1049/joe.2017.0626
[CrossRef]

[9] A support vector machine based fault diagnostics of Induction motors for practical situation of multi-sensor limited data case, Gangsar, Purushottam, Tiwari, Rajiv, Measurement, ISSN 0263-2241, Issue , 2019.
Digital Object Identifier: 10.1016/j.measurement.2018.12.011
[CrossRef]

[10] 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]

[11] A Feature Extraction Method for P2P Botnet Detection Using Graphic Symmetry Concept, Yang, Zhixian, Wang, Buhong, Symmetry, ISSN 2073-8994, Issue 3, Volume 11, 2019.
Digital Object Identifier: 10.3390/sym11030326
[CrossRef]

[12] 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]

[13] Fractional fuzzy 2DLDA approach for pomegranate fruit grade classification, Gurubelli, Yogeswararao, Ramanathan, Malmathanraj, Ponnusamy, Palanisamy, Computers and Electronics in Agriculture, ISSN 0168-1699, Issue , 2019.
Digital Object Identifier: 10.1016/j.compag.2019.03.036
[CrossRef]

[14] A novel gas turbine fault diagnosis method based on transfer learning with CNN, Zhong, Shi-sheng, Fu, Song, Lin, Lin, Measurement, ISSN 0263-2241, Issue , 2019.
Digital Object Identifier: 10.1016/j.measurement.2019.01.022
[CrossRef]

[15] Mechanical Fault Diagnosis of HVCBs Based on Multi-Feature Entropy Fusion and Hybrid Classifier, Wan, Shuting, Chen, Lei, Dou, Longjiang, Zhou, Jianping, Entropy, ISSN 1099-4300, Issue 11, Volume 20, 2018.
Digital Object Identifier: 10.3390/e20110847
[CrossRef]

[16] Improving the Performance of Storage Tank Fault Diagnosis by Removing Unwanted Components and Utilizing Wavelet-Based Features, Tra, Viet, Duong, Bach-Phi, Kim, Jae-Young, Sohaib, Muhammad, Kim, Jong-Myon, Entropy, ISSN 1099-4300, Issue 2, Volume 21, 2019.
Digital Object Identifier: 10.3390/e21020145
[CrossRef]

[17] 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]

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


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