A Hybrid Approach for Short-Term Forecasting of Wind Speed
We propose a hybrid method for forecasting the wind speed. The wind speed data is first decomposed into intrinsic mode functions (IMFs) with empirical mode decomposition. Based on the partial autocorrelation factor of the individual IMFs, adaptive methods are then employed for the prediction of IMFs...
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| Veröffentlicht in: | TheScientificWorld Jg. 2013; H. 2013; S. 1 - 8 |
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| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Cairo, Egypt
Hindawi Publishing Corporation
01.01.2013
John Wiley & Sons, Inc Wiley |
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| ISSN: | 2356-6140, 1537-744X, 1537-744X |
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| Abstract | We propose a hybrid method for forecasting the wind speed. The wind speed data is first decomposed into intrinsic mode functions (IMFs) with empirical mode decomposition. Based on the partial autocorrelation factor of the individual IMFs, adaptive methods are then employed for the prediction of IMFs. Least squares-support vector machines are employed for IMFs with weak correlation factor, and autoregressive model with Kalman filter is employed for IMFs with high correlation factor. Multistep prediction with the proposed hybrid method resulted in improved forecasting. Results with wind speed data show that the proposed method provides better forecasting compared to the existing methods. |
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| AbstractList | We propose a hybrid method for forecasting the wind speed. The wind speed data is first decomposed into intrinsic mode functions (IMFs) with empirical mode decomposition. Based on the partial autocorrelation factor of the individual IMFs, adaptive methods are then employed for the prediction of IMFs. Least squares-support vector machines are employed for IMFs with weak correlation factor, and autoregressive model with Kalman filter is employed for IMFs with high correlation factor. Multistep prediction with the proposed hybrid method resulted in improved forecasting. Results with wind speed data show that the proposed method provides better forecasting compared to the existing methods. We propose a hybrid method for forecasting the wind speed. The wind speed data is first decomposed into intrinsic mode functions (IMFs) with empirical mode decomposition. Based on the partial autocorrelation factor of the individual IMFs, adaptive methods are then employed for the prediction of IMFs. Least squares-support vector machines are employed for IMFs with weak correlation factor, and autoregressive model with Kalman filter is employed for IMFs with high correlation factor. Multistep prediction with the proposed hybrid method resulted in improved forecasting. Results with wind speed data show that the proposed method provides better forecasting compared to the existing methods.We propose a hybrid method for forecasting the wind speed. The wind speed data is first decomposed into intrinsic mode functions (IMFs) with empirical mode decomposition. Based on the partial autocorrelation factor of the individual IMFs, adaptive methods are then employed for the prediction of IMFs. Least squares-support vector machines are employed for IMFs with weak correlation factor, and autoregressive model with Kalman filter is employed for IMFs with high correlation factor. Multistep prediction with the proposed hybrid method resulted in improved forecasting. Results with wind speed data show that the proposed method provides better forecasting compared to the existing methods. |
| Author | Veluvolu, Kalyana C. Tatinati, Sivanagaraja |
| AuthorAffiliation | School of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, Republic of Korea |
| AuthorAffiliation_xml | – name: School of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, Republic of Korea |
| Author_xml | – sequence: 1 fullname: Tatinati, Sivanagaraja – sequence: 2 fullname: Veluvolu, Kalyana C. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24453872$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1016_j_ijhydene_2021_10_154 crossref_primary_10_1155_2014_438132 crossref_primary_10_1155_2014_624017 crossref_primary_10_3390_en12020254 crossref_primary_10_1103_PhysRevApplied_9_064042 crossref_primary_10_3390_app13074572 crossref_primary_10_1016_j_asoc_2018_07_041 crossref_primary_10_1016_j_egyr_2019_05_007 crossref_primary_10_1016_j_ins_2019_07_074 crossref_primary_10_1016_j_renene_2014_11_084 crossref_primary_10_1016_j_procs_2021_01_031 crossref_primary_10_1155_2014_567246 |
| Cites_doi | 10.1016/j.renene.2008.03.014 10.1016/j.renene.2003.11.009 10.1016/j.apenergy.2010.10.031 10.1100/tsw.2011.95 10.1016/j.enpol.2005.05.011 10.1016/j.apenergy.2011.01.037 10.1016/j.renene.2011.06.023 10.1016/j.renene.2013.05.012 10.1155/2013/126428 10.1175/1520-0493(1985)113<1524:GEMAMO>2.0.CO;2 10.1109/TEC.2003.821865 10.1016/j.oceaneng.2004.03.007 10.1016/j.renene.2008.09.006 10.1155/2010/684742 10.1016/j.bspc.2009.06.001 10.1016/j.enconman.2009.01.007 10.1098/rspa.1998.0193 10.1002/rcs.340 10.1016/j.jweia.2008.03.013 10.1016/j.apenergy.2012.05.029 10.3390/s110605931 10.1016/j.renene.2006.12.001 10.1016/j.apenergy.2012.04.001 10.1016/S0893-6080(00)00077-0 10.1016/S0301-4215(02)00250-1 |
| ContentType | Journal Article |
| Copyright | Copyright © 2013 Sivanagaraja Tatinati and Kalyana C. Veluvolu. Copyright © 2013 Sivanagaraja Tatinati and Kalyana C. Veluvolu. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 Copyright © 2013 S. Tatinati and K. C. Veluvolu. 2013 |
| Copyright_xml | – notice: Copyright © 2013 Sivanagaraja Tatinati and Kalyana C. Veluvolu. – notice: Copyright © 2013 Sivanagaraja Tatinati and Kalyana C. Veluvolu. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 – notice: Copyright © 2013 S. Tatinati and K. C. Veluvolu. 2013 |
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| SubjectTerms | Algorithms Alternative energy sources Autoregressive models Correlation coefficients Decomposition Forecasting Forecasting - methods Indexing in process Kalman filters Least-Squares Analysis Meteorology Models, Statistical Models, Theoretical Regression Analysis Support Vector Machine Support vector machines Wind Wind speed |
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| Title | A Hybrid Approach for Short-Term Forecasting of Wind Speed |
| URI | https://search.emarefa.net/detail/BIM-1033054 https://dx.doi.org/10.1155/2013/548370 https://www.ncbi.nlm.nih.gov/pubmed/24453872 https://www.proquest.com/docview/1564757108 https://www.proquest.com/docview/1492685153 https://www.proquest.com/docview/1508759324 https://pubmed.ncbi.nlm.nih.gov/PMC3886598 https://doaj.org/article/3e09275a050d40df8f368a1148179e37 |
| Volume | 2013 |
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