RETRACTED ARTICLE: New SVM kernel soft computing models for wind speed prediction in renewable energy applications
This paper proposes a hybrid multi-step wind speed prediction model based on combination of singular spectrum analysis (SSA), variational mode decomposition (VMD) and support vector machine (SVM) and was applied for sustainable renewable energy application. In the proposed SSA–VMD–SVM model, the SSA...
Gespeichert in:
| Veröffentlicht in: | Soft computing (Berlin, Germany) Germany), 2020-08, Vol.24 (15), p.11441-11458 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 11458 |
|---|---|
| container_issue | 15 |
| container_start_page | 11441 |
| container_title | Soft computing (Berlin, Germany) |
| container_volume | 24 |
| creator | Natarajan, Yogambal Jayalakshmi Subramaniam Nachimuthu, Deepa |
| description | This paper proposes a hybrid multi-step wind speed prediction model based on combination of singular spectrum analysis (SSA), variational mode decomposition (VMD) and support vector machine (SVM) and was applied for sustainable renewable energy application. In the proposed SSA–VMD–SVM model, the SSA was applied to eliminate the noise and to approximate the signal with trend information; VMD was applied to decompose and to extract the features of input time series wind speed data into a number of sub-layers; and the SVM model with various kernel functions was adopted to predict the wind speed from each of the sub-layers, and the parameters of SVM were fine-tuned by differential evolutionary algorithm. To investigate the effectiveness of the proposed model, various prediction models are considered for comparative study, and it is demonstrated that the proposed model outperforms with better prediction accuracy. |
| doi_str_mv | 10.1007/s00500-019-04608-w |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2917936397</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2917936397</sourcerecordid><originalsourceid>FETCH-LOGICAL-c115w-711a4fd19acdf941d9d17cb44e3305e819340e6eb43bd5b3321547e4614d10373</originalsourceid><addsrcrecordid>eNp9kF1LwzAUhosoOKd_wKuA19WcJm0W70atOpgKs3ob2uZ0dHZNTTrK_r3dKnjn1XnhvB_weN410FugVNw5SkNKfQrSpzyiM78_8SbAGfMFF_L0qANfRJydexfObSgNQIRs4tlVkq7mcZo8kPkqXcTL5J68Yk_eP1_IF9oGa-JM2ZHCbNtdVzVrsjUaa0dKY0lfNZq4FlGT1qKuiq4yDakaYrHBPstrJIOw6z3J2rauiuzwd5feWZnVDq9-79T7eEzS-Nlfvj0t4vnSLwDC3hcAGS81yKzQpeSgpQZR5JwjYzTEGUjGKUaYc5brMGcsgJAL5BFwDZQJNvVuxt7Wmu8duk5tzM42w6QKJAjJIiYPrmB0FdY4Z7FUra22md0roOrAVo1s1cBWHdmqfgixMeQGc7NG-1f9T-oHZKl7-w</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2917936397</pqid></control><display><type>article</type><title>RETRACTED ARTICLE: New SVM kernel soft computing models for wind speed prediction in renewable energy applications</title><source>Springer Nature - Complete Springer Journals</source><source>ProQuest Central UK/Ireland</source><source>ProQuest Central</source><creator>Natarajan, Yogambal Jayalakshmi ; Subramaniam Nachimuthu, Deepa</creator><creatorcontrib>Natarajan, Yogambal Jayalakshmi ; Subramaniam Nachimuthu, Deepa</creatorcontrib><description>This paper proposes a hybrid multi-step wind speed prediction model based on combination of singular spectrum analysis (SSA), variational mode decomposition (VMD) and support vector machine (SVM) and was applied for sustainable renewable energy application. In the proposed SSA–VMD–SVM model, the SSA was applied to eliminate the noise and to approximate the signal with trend information; VMD was applied to decompose and to extract the features of input time series wind speed data into a number of sub-layers; and the SVM model with various kernel functions was adopted to predict the wind speed from each of the sub-layers, and the parameters of SVM were fine-tuned by differential evolutionary algorithm. To investigate the effectiveness of the proposed model, various prediction models are considered for comparative study, and it is demonstrated that the proposed model outperforms with better prediction accuracy.</description><identifier>ISSN: 1432-7643</identifier><identifier>EISSN: 1433-7479</identifier><identifier>DOI: 10.1007/s00500-019-04608-w</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Accuracy ; Algorithms ; Alternative energy ; Artificial Intelligence ; Back propagation ; Comparative studies ; Computational Intelligence ; Control ; Decomposition ; Engineering ; Entropy ; Evolutionary algorithms ; Forecasting ; Kernel functions ; Mathematical Logic and Foundations ; Mechatronics ; Methodologies and Application ; Neural networks ; Prediction models ; Renewable energy ; Renewable resources ; Robotics ; Soft computing ; Spectrum analysis ; Support vector machines ; Time series ; Wind speed</subject><ispartof>Soft computing (Berlin, Germany), 2020-08, Vol.24 (15), p.11441-11458</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2019. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c115w-711a4fd19acdf941d9d17cb44e3305e819340e6eb43bd5b3321547e4614d10373</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00500-019-04608-w$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2917936397?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,776,780,21368,27903,27904,33723,41467,42536,43784,51297,64361,64365,72215</link.rule.ids></links><search><creatorcontrib>Natarajan, Yogambal Jayalakshmi</creatorcontrib><creatorcontrib>Subramaniam Nachimuthu, Deepa</creatorcontrib><title>RETRACTED ARTICLE: New SVM kernel soft computing models for wind speed prediction in renewable energy applications</title><title>Soft computing (Berlin, Germany)</title><addtitle>Soft Comput</addtitle><description>This paper proposes a hybrid multi-step wind speed prediction model based on combination of singular spectrum analysis (SSA), variational mode decomposition (VMD) and support vector machine (SVM) and was applied for sustainable renewable energy application. In the proposed SSA–VMD–SVM model, the SSA was applied to eliminate the noise and to approximate the signal with trend information; VMD was applied to decompose and to extract the features of input time series wind speed data into a number of sub-layers; and the SVM model with various kernel functions was adopted to predict the wind speed from each of the sub-layers, and the parameters of SVM were fine-tuned by differential evolutionary algorithm. To investigate the effectiveness of the proposed model, various prediction models are considered for comparative study, and it is demonstrated that the proposed model outperforms with better prediction accuracy.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Alternative energy</subject><subject>Artificial Intelligence</subject><subject>Back propagation</subject><subject>Comparative studies</subject><subject>Computational Intelligence</subject><subject>Control</subject><subject>Decomposition</subject><subject>Engineering</subject><subject>Entropy</subject><subject>Evolutionary algorithms</subject><subject>Forecasting</subject><subject>Kernel functions</subject><subject>Mathematical Logic and Foundations</subject><subject>Mechatronics</subject><subject>Methodologies and Application</subject><subject>Neural networks</subject><subject>Prediction models</subject><subject>Renewable energy</subject><subject>Renewable resources</subject><subject>Robotics</subject><subject>Soft computing</subject><subject>Spectrum analysis</subject><subject>Support vector machines</subject><subject>Time series</subject><subject>Wind speed</subject><issn>1432-7643</issn><issn>1433-7479</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kF1LwzAUhosoOKd_wKuA19WcJm0W70atOpgKs3ob2uZ0dHZNTTrK_r3dKnjn1XnhvB_weN410FugVNw5SkNKfQrSpzyiM78_8SbAGfMFF_L0qANfRJydexfObSgNQIRs4tlVkq7mcZo8kPkqXcTL5J68Yk_eP1_IF9oGa-JM2ZHCbNtdVzVrsjUaa0dKY0lfNZq4FlGT1qKuiq4yDakaYrHBPstrJIOw6z3J2rauiuzwd5feWZnVDq9-79T7eEzS-Nlfvj0t4vnSLwDC3hcAGS81yKzQpeSgpQZR5JwjYzTEGUjGKUaYc5brMGcsgJAL5BFwDZQJNvVuxt7Wmu8duk5tzM42w6QKJAjJIiYPrmB0FdY4Z7FUra22md0roOrAVo1s1cBWHdmqfgixMeQGc7NG-1f9T-oHZKl7-w</recordid><startdate>20200801</startdate><enddate>20200801</enddate><creator>Natarajan, Yogambal Jayalakshmi</creator><creator>Subramaniam Nachimuthu, Deepa</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20200801</creationdate><title>RETRACTED ARTICLE: New SVM kernel soft computing models for wind speed prediction in renewable energy applications</title><author>Natarajan, Yogambal Jayalakshmi ; Subramaniam Nachimuthu, Deepa</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c115w-711a4fd19acdf941d9d17cb44e3305e819340e6eb43bd5b3321547e4614d10373</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Alternative energy</topic><topic>Artificial Intelligence</topic><topic>Back propagation</topic><topic>Comparative studies</topic><topic>Computational Intelligence</topic><topic>Control</topic><topic>Decomposition</topic><topic>Engineering</topic><topic>Entropy</topic><topic>Evolutionary algorithms</topic><topic>Forecasting</topic><topic>Kernel functions</topic><topic>Mathematical Logic and Foundations</topic><topic>Mechatronics</topic><topic>Methodologies and Application</topic><topic>Neural networks</topic><topic>Prediction models</topic><topic>Renewable energy</topic><topic>Renewable resources</topic><topic>Robotics</topic><topic>Soft computing</topic><topic>Spectrum analysis</topic><topic>Support vector machines</topic><topic>Time series</topic><topic>Wind speed</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Natarajan, Yogambal Jayalakshmi</creatorcontrib><creatorcontrib>Subramaniam Nachimuthu, Deepa</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Soft computing (Berlin, Germany)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Natarajan, Yogambal Jayalakshmi</au><au>Subramaniam Nachimuthu, Deepa</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>RETRACTED ARTICLE: New SVM kernel soft computing models for wind speed prediction in renewable energy applications</atitle><jtitle>Soft computing (Berlin, Germany)</jtitle><stitle>Soft Comput</stitle><date>2020-08-01</date><risdate>2020</risdate><volume>24</volume><issue>15</issue><spage>11441</spage><epage>11458</epage><pages>11441-11458</pages><issn>1432-7643</issn><eissn>1433-7479</eissn><abstract>This paper proposes a hybrid multi-step wind speed prediction model based on combination of singular spectrum analysis (SSA), variational mode decomposition (VMD) and support vector machine (SVM) and was applied for sustainable renewable energy application. In the proposed SSA–VMD–SVM model, the SSA was applied to eliminate the noise and to approximate the signal with trend information; VMD was applied to decompose and to extract the features of input time series wind speed data into a number of sub-layers; and the SVM model with various kernel functions was adopted to predict the wind speed from each of the sub-layers, and the parameters of SVM were fine-tuned by differential evolutionary algorithm. To investigate the effectiveness of the proposed model, various prediction models are considered for comparative study, and it is demonstrated that the proposed model outperforms with better prediction accuracy.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00500-019-04608-w</doi><tpages>18</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1432-7643 |
| ispartof | Soft computing (Berlin, Germany), 2020-08, Vol.24 (15), p.11441-11458 |
| issn | 1432-7643 1433-7479 |
| language | eng |
| recordid | cdi_proquest_journals_2917936397 |
| source | Springer Nature - Complete Springer Journals; ProQuest Central UK/Ireland; ProQuest Central |
| subjects | Accuracy Algorithms Alternative energy Artificial Intelligence Back propagation Comparative studies Computational Intelligence Control Decomposition Engineering Entropy Evolutionary algorithms Forecasting Kernel functions Mathematical Logic and Foundations Mechatronics Methodologies and Application Neural networks Prediction models Renewable energy Renewable resources Robotics Soft computing Spectrum analysis Support vector machines Time series Wind speed |
| title | RETRACTED ARTICLE: New SVM kernel soft computing models for wind speed prediction in renewable energy applications |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-26T20%3A08%3A05IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=RETRACTED%20ARTICLE:%20New%20SVM%20kernel%20soft%20computing%20models%20for%20wind%20speed%20prediction%20in%20renewable%20energy%20applications&rft.jtitle=Soft%20computing%20(Berlin,%20Germany)&rft.au=Natarajan,%20Yogambal%20Jayalakshmi&rft.date=2020-08-01&rft.volume=24&rft.issue=15&rft.spage=11441&rft.epage=11458&rft.pages=11441-11458&rft.issn=1432-7643&rft.eissn=1433-7479&rft_id=info:doi/10.1007/s00500-019-04608-w&rft_dat=%3Cproquest_cross%3E2917936397%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2917936397&rft_id=info:pmid/&rfr_iscdi=true |