A Method Based on Numerical Wind Field and Extreme Learning Machine for Typhoon Wind Speed Prediction of Wind Farm
Typhoon wind speed prediction is of great significance for it can help prevent wind farms from damages caused by frequent typhoon disasters in coastal areas. However, most researches on wind forecast are either for meteorological application or for normal weather. Therefore, this paper proposes a sy...
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| Veröffentlicht in: | Mathematical problems in engineering 2021-11, Vol.2021, p.1-15 |
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| creator | Xu, Hong Wang, Wan-Yu |
| description | Typhoon wind speed prediction is of great significance for it can help prevent wind farms from damages caused by frequent typhoon disasters in coastal areas. However, most researches on wind forecast are either for meteorological application or for normal weather. Therefore, this paper proposes a systematic method based on numerical wind field and extreme learning machine for typhoon wind speed prediction of wind farms. The proposed method mainly consists of three parts, IGA-YanMeng typhoon numerical simulation model, typhoon status prediction model, and wind speed simulation model based on an extreme learning machine. The IGA-YanMeng typhoon numerical simulation model can greatly enrich typhoon wind speed data according to historical typhoon parameters. The typhoon status prediction model can predict the status of typhoons studied in the next few hours. And wind speed simulation model simulates the average wind speed magnitude/direction at 10 m height of each turbine in the farm according to the predicted status. The end of this paper presents a case study on a wind farm located in Guangdong province that suffered from the super typhoon Mangkhut landed in 2018. The results verified the feasibility and effectiveness of the proposed method. |
| doi_str_mv | 10.1155/2021/7147973 |
| format | Article |
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However, most researches on wind forecast are either for meteorological application or for normal weather. Therefore, this paper proposes a systematic method based on numerical wind field and extreme learning machine for typhoon wind speed prediction of wind farms. The proposed method mainly consists of three parts, IGA-YanMeng typhoon numerical simulation model, typhoon status prediction model, and wind speed simulation model based on an extreme learning machine. The IGA-YanMeng typhoon numerical simulation model can greatly enrich typhoon wind speed data according to historical typhoon parameters. The typhoon status prediction model can predict the status of typhoons studied in the next few hours. And wind speed simulation model simulates the average wind speed magnitude/direction at 10 m height of each turbine in the farm according to the predicted status. The end of this paper presents a case study on a wind farm located in Guangdong province that suffered from the super typhoon Mangkhut landed in 2018. The results verified the feasibility and effectiveness of the proposed method.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2021/7147973</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Artificial neural networks ; Coastal zone ; Damage prevention ; Data processing ; Friction ; Machine learning ; Mathematical models ; Neural networks ; Prediction models ; Rain ; Simulation ; Turbines ; Typhoons ; Weather forecasting ; Wind damage ; Wind farms ; Wind power ; Wind speed</subject><ispartof>Mathematical problems in engineering, 2021-11, Vol.2021, p.1-15</ispartof><rights>Copyright © 2021 Hong Xu and Wan-Yu Wang.</rights><rights>Copyright © 2021 Hong Xu and Wan-Yu Wang. 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</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c337t-bb895756ea429ab1363d1864804f0f2f5436d2237a2bf1dc6be9e166bd4791e3</citedby><cites>FETCH-LOGICAL-c337t-bb895756ea429ab1363d1864804f0f2f5436d2237a2bf1dc6be9e166bd4791e3</cites><orcidid>0000-0001-6005-7314</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><contributor>Pan, Hongguang</contributor><contributor>Hongguang Pan</contributor><creatorcontrib>Xu, Hong</creatorcontrib><creatorcontrib>Wang, Wan-Yu</creatorcontrib><title>A Method Based on Numerical Wind Field and Extreme Learning Machine for Typhoon Wind Speed Prediction of Wind Farm</title><title>Mathematical problems in engineering</title><description>Typhoon wind speed prediction is of great significance for it can help prevent wind farms from damages caused by frequent typhoon disasters in coastal areas. However, most researches on wind forecast are either for meteorological application or for normal weather. Therefore, this paper proposes a systematic method based on numerical wind field and extreme learning machine for typhoon wind speed prediction of wind farms. The proposed method mainly consists of three parts, IGA-YanMeng typhoon numerical simulation model, typhoon status prediction model, and wind speed simulation model based on an extreme learning machine. The IGA-YanMeng typhoon numerical simulation model can greatly enrich typhoon wind speed data according to historical typhoon parameters. The typhoon status prediction model can predict the status of typhoons studied in the next few hours. And wind speed simulation model simulates the average wind speed magnitude/direction at 10 m height of each turbine in the farm according to the predicted status. The end of this paper presents a case study on a wind farm located in Guangdong province that suffered from the super typhoon Mangkhut landed in 2018. The results verified the feasibility and effectiveness of the proposed method.</description><subject>Artificial neural networks</subject><subject>Coastal zone</subject><subject>Damage prevention</subject><subject>Data processing</subject><subject>Friction</subject><subject>Machine learning</subject><subject>Mathematical models</subject><subject>Neural networks</subject><subject>Prediction models</subject><subject>Rain</subject><subject>Simulation</subject><subject>Turbines</subject><subject>Typhoons</subject><subject>Weather forecasting</subject><subject>Wind damage</subject><subject>Wind farms</subject><subject>Wind power</subject><subject>Wind speed</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>BENPR</sourceid><recordid>eNp9kM1OwzAQhC0EEqVw4wEscYSAf2InOZaqBaQWkKgEt8iJ19RVEwc7FfTtcWnPnHZ29e2sdhC6pOSWUiHuGGH0LqNpVmT8CA2okDwRsT2OmrA0oYx_nKKzEFYkkoLmA-RHeA790ml8rwJo7Fr8vGnA21qt8bttNZ5aWGusopr89B4awDNQvrXtJ56remlbwMZ5vNh2Sxe3_3beOoherx60rXsbp84czJRvztGJUesAF4c6RIvpZDF-TGYvD0_j0SypOc_6pKryQmRCgkpZoSrKJdc0l2lOUkMMMyLlUjPGM8UqQ3UtKyiASlnp-D8FPkRXe9vOu68NhL5cuY1v48WSScJFQQnnkbrZU7V3IXgwZedto_y2pKTchVruQi0PoUb8eo_Hv7X6tv_Tv7HhdRA</recordid><startdate>20211120</startdate><enddate>20211120</enddate><creator>Xu, Hong</creator><creator>Wang, Wan-Yu</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0001-6005-7314</orcidid></search><sort><creationdate>20211120</creationdate><title>A Method Based on Numerical Wind Field and Extreme Learning Machine for Typhoon Wind Speed Prediction of Wind Farm</title><author>Xu, Hong ; Wang, Wan-Yu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c337t-bb895756ea429ab1363d1864804f0f2f5436d2237a2bf1dc6be9e166bd4791e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Artificial neural networks</topic><topic>Coastal zone</topic><topic>Damage prevention</topic><topic>Data processing</topic><topic>Friction</topic><topic>Machine learning</topic><topic>Mathematical models</topic><topic>Neural networks</topic><topic>Prediction models</topic><topic>Rain</topic><topic>Simulation</topic><topic>Turbines</topic><topic>Typhoons</topic><topic>Weather forecasting</topic><topic>Wind damage</topic><topic>Wind farms</topic><topic>Wind power</topic><topic>Wind speed</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xu, Hong</creatorcontrib><creatorcontrib>Wang, Wan-Yu</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access Journals</collection><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</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>Middle East & Africa Database</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><jtitle>Mathematical problems in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xu, Hong</au><au>Wang, Wan-Yu</au><au>Pan, Hongguang</au><au>Hongguang Pan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Method Based on Numerical Wind Field and Extreme Learning Machine for Typhoon Wind Speed Prediction of Wind Farm</atitle><jtitle>Mathematical problems in engineering</jtitle><date>2021-11-20</date><risdate>2021</risdate><volume>2021</volume><spage>1</spage><epage>15</epage><pages>1-15</pages><issn>1024-123X</issn><eissn>1563-5147</eissn><abstract>Typhoon wind speed prediction is of great significance for it can help prevent wind farms from damages caused by frequent typhoon disasters in coastal areas. However, most researches on wind forecast are either for meteorological application or for normal weather. Therefore, this paper proposes a systematic method based on numerical wind field and extreme learning machine for typhoon wind speed prediction of wind farms. The proposed method mainly consists of three parts, IGA-YanMeng typhoon numerical simulation model, typhoon status prediction model, and wind speed simulation model based on an extreme learning machine. The IGA-YanMeng typhoon numerical simulation model can greatly enrich typhoon wind speed data according to historical typhoon parameters. The typhoon status prediction model can predict the status of typhoons studied in the next few hours. And wind speed simulation model simulates the average wind speed magnitude/direction at 10 m height of each turbine in the farm according to the predicted status. The end of this paper presents a case study on a wind farm located in Guangdong province that suffered from the super typhoon Mangkhut landed in 2018. The results verified the feasibility and effectiveness of the proposed method.</abstract><cop>New York</cop><pub>Hindawi</pub><doi>10.1155/2021/7147973</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0001-6005-7314</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Artificial neural networks Coastal zone Damage prevention Data processing Friction Machine learning Mathematical models Neural networks Prediction models Rain Simulation Turbines Typhoons Weather forecasting Wind damage Wind farms Wind power Wind speed |
| title | A Method Based on Numerical Wind Field and Extreme Learning Machine for Typhoon Wind Speed Prediction of Wind Farm |
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