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Wind power prediction using time-series analysis base on rough sets
In long-term prediction, dealing with the relevant factors correctly is the key point to improve the wind power prediction accuracy. The key factors that affect the wind power prediction are identified by rough set theory and then the additional inputs of the prediction model are determined. To test...
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| creator | Gao Shuang Dong Lei Tian Chengwei Liao Xiaozhong |
| description | In long-term prediction, dealing with the relevant factors correctly is the key point to improve the wind power prediction accuracy. The key factors that affect the wind power prediction are identified by rough set theory and then the additional inputs of the prediction model are determined. To test the approach, the weather data from Beijing area are used for this study. The prediction results are presented and compared to the chaos neural network model and persistence model. The results show that rough set method will be a useful tool in longterm prediction of wind power. |
| doi_str_mv | 10.1109/ICEICE.2011.5777058 |
| format | conference_proceeding |
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The key factors that affect the wind power prediction are identified by rough set theory and then the additional inputs of the prediction model are determined. To test the approach, the weather data from Beijing area are used for this study. The prediction results are presented and compared to the chaos neural network model and persistence model. 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The key factors that affect the wind power prediction are identified by rough set theory and then the additional inputs of the prediction model are determined. To test the approach, the weather data from Beijing area are used for this study. The prediction results are presented and compared to the chaos neural network model and persistence model. The results show that rough set method will be a useful tool in longterm prediction of wind power.</description><subject>Artificial neural networks</subject><subject>Chaos</subject><subject>Information systems</subject><subject>neural network</subject><subject>Numerical models</subject><subject>prediction model</subject><subject>Predictive models</subject><subject>rough set</subject><subject>Wind power generation</subject><subject>wind power prediction</subject><subject>Wind speed</subject><isbn>1424480361</isbn><isbn>9781424480364</isbn><isbn>1424480396</isbn><isbn>9781424480395</isbn><isbn>1424480388</isbn><isbn>9781424480388</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNpFj1FLwzAUhSMiqHO_YC_5A61Jm94kj1KmDga-DHwcN-ntjGxt6e2Q_XsLDjwcOHxwOHCEWGmVa63886Zez84LpXVeWWtV5W7EozaFMU6VHm7_AfS9WDJ_q1kAXlv1IOrP1DVy6H9olMNITYpT6jt55tQd5JROlDGNiVhih8cLJ5YBmeRcGfvz4UsyTfwk7lo8Mi2vuRC71_Wufs-2H2-b-mWbJa-mzFhCIoNILhiA6ANoGwBCJLK-KZxtnSuC10GBBzQRKxetw9ZYBwpjuRCrv9lERPthTCccL_vr5_IXtgpL_g</recordid><startdate>201104</startdate><enddate>201104</enddate><creator>Gao Shuang</creator><creator>Dong Lei</creator><creator>Tian Chengwei</creator><creator>Liao Xiaozhong</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201104</creationdate><title>Wind power prediction using time-series analysis base on rough sets</title><author>Gao Shuang ; Dong Lei ; Tian Chengwei ; Liao Xiaozhong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-47eaee4aae8b466c9b617b66bcee79d287f882b91b0696a4ca58c78af47860ac3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Artificial neural networks</topic><topic>Chaos</topic><topic>Information systems</topic><topic>neural network</topic><topic>Numerical models</topic><topic>prediction model</topic><topic>Predictive models</topic><topic>rough set</topic><topic>Wind power generation</topic><topic>wind power prediction</topic><topic>Wind speed</topic><toplevel>online_resources</toplevel><creatorcontrib>Gao Shuang</creatorcontrib><creatorcontrib>Dong Lei</creatorcontrib><creatorcontrib>Tian Chengwei</creatorcontrib><creatorcontrib>Liao Xiaozhong</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Gao Shuang</au><au>Dong Lei</au><au>Tian Chengwei</au><au>Liao Xiaozhong</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Wind power prediction using time-series analysis base on rough sets</atitle><btitle>2011 International Conference on Electric Information and Control Engineering</btitle><stitle>ICEICE</stitle><date>2011-04</date><risdate>2011</risdate><spage>2847</spage><epage>2852</epage><pages>2847-2852</pages><isbn>1424480361</isbn><isbn>9781424480364</isbn><eisbn>1424480396</eisbn><eisbn>9781424480395</eisbn><eisbn>1424480388</eisbn><eisbn>9781424480388</eisbn><abstract>In long-term prediction, dealing with the relevant factors correctly is the key point to improve the wind power prediction accuracy. The key factors that affect the wind power prediction are identified by rough set theory and then the additional inputs of the prediction model are determined. To test the approach, the weather data from Beijing area are used for this study. The prediction results are presented and compared to the chaos neural network model and persistence model. The results show that rough set method will be a useful tool in longterm prediction of wind power.</abstract><pub>IEEE</pub><doi>10.1109/ICEICE.2011.5777058</doi><tpages>6</tpages></addata></record> |
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| ispartof | 2011 International Conference on Electric Information and Control Engineering, 2011, p.2847-2852 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks Chaos Information systems neural network Numerical models prediction model Predictive models rough set Wind power generation wind power prediction Wind speed |
| title | Wind power prediction using time-series analysis base on rough sets |
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