|2/2018 - 1|
Improved Wind Speed Prediction Using Empirical Mode DecompositionZHANG, Y. , ZHANG, C. , SUN, J. , GUO, J.
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,055 KB) | Citation | Downloads: 1,091 | Views: 26,730|
renewable energy, wind speed prediction, empirical mode decomposition, radial basis function neural network, least squares support vector basis
wind(15), prediction(15), energy(10), speed(7), artificial(7), system(5), model(5), forecasting(5), time(4), term(4)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2018-05-31
Volume 18, Issue 2, Year 2018, On page(s): 3 - 10
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.02001
Web of Science Accession Number: 000434245000001
SCOPUS ID: 85047879257
Wind power industry plays an important role in promoting the development of low-carbon economic and energy transformation in the world. However, the randomness and volatility of wind speed series restrict the healthy development of the wind power industry. Accurate wind speed prediction is the key to realize the stability of wind power integration and to guarantee the safe operation of the power system. In this paper, combined with the Empirical Mode Decomposition (EMD), the Radial Basis Function Neural Network (RBF) and the Least Square Support Vector Machine (SVM), an improved wind speed prediction model based on Empirical Mode Decomposition (EMD-RBF-LS-SVM) is proposed. The prediction result indicates that compared with the traditional prediction model (RBF, LS-SVM), the EMD-RBF-LS-SVM model can weaken the random fluctuation to a certain extent and improve the short-term accuracy of wind speed prediction significantly. In a word, this research will significantly reduce the impact of wind power instability on the power grid, ensure the power grid supply and demand balance, reduce the operating costs in the grid-connected systems, and enhance the market competitiveness of the wind power.
Web of Science® Times Cited: 36 [View]
View record in Web of Science® [View]
View Related Records® [View]
SCOPUS® Times Cited: 33
View record in SCOPUS® [Free preview]
View citations in SCOPUS® [Free preview]
 Wear indicator construction of rolling bearings based on multi-channel deep convolutional neural network with exponentially decaying learning rate, She, Daoming, Jia, Minping, Measurement, ISSN 0263-2241, Issue , 2019.
Digital Object Identifier: 10.1016/j.measurement.2018.11.040 [CrossRef]
 Health monitoring of bearing and gear faults by using a new health indicator extracted from current signals, Soualhi, Moncef, Nguyen, Khanh T.P., Soualhi, Abdenour, Medjaher, Kamal, Hemsas, Kamel Eddine, Measurement, ISSN 0263-2241, Issue , 2019.
Digital Object Identifier: 10.1016/j.measurement.2019.03.065 [CrossRef]
 A new multistage short-term wind power forecast model using decomposition and artificial intelligence methods, Çevik, Hasan Hüseyin, Çunkaş, Mehmet, Polat, Kemal, Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371, Issue , 2019.
Digital Object Identifier: 10.1016/j.physa.2019.122177 [CrossRef]
 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]
 Early Fault Detection Method for Rotating Machinery Based on Harmonic-Assisted Multivariate Empirical Mode Decomposition and Transfer Entropy, Wu, Zhe, Zhang, Qiang, Wang, Lixin, Cheng, Lifeng, Zhou, Jingbo, Entropy, ISSN 1099-4300, Issue 11, Volume 20, 2018.
Digital Object Identifier: 10.3390/e20110873 [CrossRef]
 NOSCNN: A robust method for fault diagnosis of RV reducer, Peng, Peng, Wang, Jiugen, Measurement, ISSN 0263-2241, Issue , 2019.
Digital Object Identifier: 10.1016/j.measurement.2019.02.080 [CrossRef]
 Short-term wind speed forecasting based on the Jaya-SVM model, Liu, Mingshuai, Cao, Zheming, Zhang, Jing, Wang, Long, Huang, Chao, Luo, Xiong, International Journal of Electrical Power & Energy Systems, ISSN 0142-0615, Issue , 2020.
Digital Object Identifier: 10.1016/j.ijepes.2020.106056 [CrossRef]
 Induction Motors Dynamic Eccentricity Fault Diagnosis Based on the Combined Use of WPD and EMD-Simulation Study, Tian, Kun, Zhang, Tao, Ai, Yibo, Zhang, Weidong, Applied Sciences, ISSN 2076-3417, Issue 10, Volume 8, 2018.
Digital Object Identifier: 10.3390/app8101709 [CrossRef]
 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]
 Incipient fault detection of wind turbine large-size slewing bearing based on circular domain, Pan, Yubin, Hong, Rongjing, Chen, Jie, Qin, Zhongwei, Feng, Yang, Measurement, ISSN 0263-2241, Issue , 2019.
Digital Object Identifier: 10.1016/j.measurement.2019.01.033 [CrossRef]
 EMD-GM-ARMA Model for Mining Safety Production Situation Prediction, Wu, Menglong, Ye, Yicheng, Hu, Nanyan, Wang, Qihu, Jiang, Huimin, Li, Wen, Complexity, ISSN 1076-2787, Issue , 2020.
Digital Object Identifier: 10.1155/2020/1341047 [CrossRef]
 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]
 A tacholess order tracking method for wind turbine planetary gearbox fault detection, Hou, Bingchang, Wang, Yi, Tang, Baoping, Qin, Yi, Chen, Yang, Chen, Yuhang, Measurement, ISSN 0263-2241, Issue , 2019.
Digital Object Identifier: 10.1016/j.measurement.2019.02.010 [CrossRef]
 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]
 Fault Severity Monitoring of Rolling Bearings Based on Texture Feature Extraction of Sparse Time–Frequency Images, Du, Yan, Chen, Yingpin, Meng, Guoying, Ding, Jun, Xiao, Yajing, Applied Sciences, ISSN 2076-3417, Issue 9, Volume 8, 2018.
Digital Object Identifier: 10.3390/app8091538 [CrossRef]
 Classification and comparison of rotor temperature estimation methods of squirrel cage induction motors, Nikbakhsh, Amir, Izadfar, Hamid Reza, Jazaeri, Mostafa, Measurement, ISSN 0263-2241, Issue , 2019.
Digital Object Identifier: 10.1016/j.measurement.2019.03.072 [CrossRef]
 Changes in rotor response characteristics based diagnostic method and its application to identification of misalignment, Qu, Lei, Lin, Jing, Liao, Yuhe, Zhao, Ming, Measurement, ISSN 0263-2241, Issue , 2019.
Digital Object Identifier: 10.1016/j.measurement.2019.01.075 [CrossRef]
 Fault diagnosis of bearing based on Symbolic Aggregate approXimation and Lempel-Ziv, Yin, Jiancheng, Xu, Minqiang, Zheng, Huailiang, Measurement, ISSN 0263-2241, Issue , 2019.
Digital Object Identifier: 10.1016/j.measurement.2019.02.011 [CrossRef]
 Fault Diagnosis of Active Magnetic Bearing–Rotor System via Vibration Images, Yan, Xunshi, Sun, Zhe, Zhao, Jingjing, Shi, Zhengang, Zhang, Chen-An, Sensors, ISSN 1424-8220, Issue 2, Volume 19, 2019.
Digital Object Identifier: 10.3390/s19020244 [CrossRef]
 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]
 Application of Mutual Information-Sample Entropy Based MED-ICEEMDAN De-Noising Scheme for Weak Fault Diagnosis of Hoist Bearing, Yang, Fen, Kou, Ziming, Wu, Juan, Li, Tengyu, Entropy, ISSN 1099-4300, Issue 9, Volume 20, 2018.
Digital Object Identifier: 10.3390/e20090667 [CrossRef]
 Learning deep representation of imbalanced SCADA data for fault detection of wind turbines, Chen, Longting, Xu, Guanghua, Zhang, Qing, Zhang, Xun, Measurement, ISSN 0263-2241, Issue , 2019.
Digital Object Identifier: 10.1016/j.measurement.2019.03.029 [CrossRef]
 Gear Backlash Detection and Evaluation Based on Current Characteristic Extraction and Selection, Yang, Qichao, Liu, Tao, Wu, Xing, Deng, Yunnan, IEEE Access, ISSN 2169-3536, Issue , 2020.
Digital Object Identifier: 10.1109/ACCESS.2020.2999478 [CrossRef]
 Electronic Systems Diagnosis Fault in Gasoline Engines Based on Multi-Information Fusion, Hu, Jie, Huang, Tengfei, Zhou, Jiaopeng, Zeng, Jiawei, Sensors, ISSN 1424-8220, Issue 9, Volume 18, 2018.
Digital Object Identifier: 10.3390/s18092917 [CrossRef]
 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]
 Application of ICEEMDAN Energy Entropy and AFSA-SVM for Fault Diagnosis of Hoist Sheave Bearing, Kou, Ziming, Yang, Fen, Wu, Juan, Li, Tengyu, Entropy, ISSN 1099-4300, Issue 12, Volume 22, 2020.
Digital Object Identifier: 10.3390/e22121347 [CrossRef]
 Rub-Impact Fault Diagnosis Using an Effective IMF Selection Technique in Ensemble Empirical Mode Decomposition and Hybrid Feature Models, Prosvirin, Alexander, Islam, Manjurul, Kim, Jaeyoung, Kim, Jong-Myon, Sensors, ISSN 1424-8220, Issue 7, Volume 18, 2018.
Digital Object Identifier: 10.3390/s18072040 [CrossRef]
 Early Fault Diagnosis for Planetary Gearbox Based Wavelet Packet Energy and Modulation Signal Bispectrum Analysis, Guo, Junchao, Shi, Zhanqun, Li, Haiyang, Zhen, Dong, Gu, Fengshou, Ball, Andrew, Sensors, ISSN 1424-8220, Issue 9, Volume 18, 2018.
Digital Object Identifier: 10.3390/s18092908 [CrossRef]
 A Hybrid Grey Prediction Model for Small Oscillation Sequence Based on Information Decomposition, Zhou, Meng, Zeng, Bo, Zhou, Wenhao, Complexity, ISSN 1076-2787, Issue , 2020.
Digital Object Identifier: 10.1155/2020/5071267 [CrossRef]
 A Hybrid SVM-LSTM Temperature Prediction Model Based on Empirical Mode Decomposition and Residual Prediction, Peng, Wenqiang, Ni, Qingjian, 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), ISBN 978-1-7281-8526-2, 2020.
Digital Object Identifier: 10.1109/SMC42975.2020.9282824 [CrossRef]
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.
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.