Short-term wind speed forecasting combined time series method and arch model
In order to improve the accuracy of the wind speed forecasting in the wind farm, this paper presents an ARIMA-ARCH model, which considers the heteroscedastic effect between the fluctuation of wind speed and the characteristics of the change of wind speed, to forecast the wind speed. First of all, th...
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| creator | Meng-Di Wang Qi-Rong Qiu Bing-Wei Cui |
| description | In order to improve the accuracy of the wind speed forecasting in the wind farm, this paper presents an ARIMA-ARCH model, which considers the heteroscedastic effect between the fluctuation of wind speed and the characteristics of the change of wind speed, to forecast the wind speed. First of all, the ARIMA model for the wind speed time series is built by SPSS. After that, the high lag order ARCH effect is found in the residual of the ARIMA model by Lagrange multiplier (LM) test. At last, the GARCH model is built for simulating the residual series and thus to construct the ARIMA-ARCH model. Numerical experiments demonstrate the superiority of the proposed method when comparing with the traditional ARIMA model. |
| doi_str_mv | 10.1109/ICMLC.2012.6359477 |
| format | Conference Proceeding |
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First of all, the ARIMA model for the wind speed time series is built by SPSS. After that, the high lag order ARCH effect is found in the residual of the ARIMA model by Lagrange multiplier (LM) test. At last, the GARCH model is built for simulating the residual series and thus to construct the ARIMA-ARCH model. 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Numerical experiments demonstrate the superiority of the proposed method when comparing with the traditional ARIMA model.</description><subject>Abstracts</subject><subject>ARCH model</subject><subject>ARIMA model</subject><subject>Atmospheric measurements</subject><subject>Pollution measurement</subject><subject>Predictive models</subject><subject>Short-term wind speed forecasting</subject><subject>Time series analysis</subject><subject>Wind forecasting</subject><subject>Wind speed</subject><issn>2160-133X</issn><isbn>1467314846</isbn><isbn>9781467314848</isbn><isbn>9781467314879</isbn><isbn>1467314870</isbn><isbn>9781467314862</isbn><isbn>1467314862</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1UMFKxDAUjKjguvYH9JIfaE2a5CU5StF1oeLBPXhbYvNqI9t2SQLi31twncswAzMMQ8gtZxXnzN5vm5e2qWrG6wqEslLrM1JYbbgELbg02p6T638h4YKsag6s5EK8X5EipS-2QEtpLF-R9m2YYy4zxpF-h8nTdET0tJ8jdi7lMH3Sbh4_wrSYOYxIE8aAiY6Yh9lTtyRc7AY6zh4PN-Syd4eExYnXZPf0uGuey_Z1s20e2jJYlksDyjDZa4PecoBlL2hlEYRkNXLopXG-qzvwoBAUMGW846Z33nZO9sqKNbn7qw2IuD_GMLr4sz99IX4BHh1QWg</recordid><startdate>201207</startdate><enddate>201207</enddate><creator>Meng-Di Wang</creator><creator>Qi-Rong Qiu</creator><creator>Bing-Wei Cui</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201207</creationdate><title>Short-term wind speed forecasting combined time series method and arch model</title><author>Meng-Di Wang ; Qi-Rong Qiu ; Bing-Wei Cui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-865804f78ed91668796759e63402e16f48adc2c6d65e656058da18fad9ca4f593</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Abstracts</topic><topic>ARCH model</topic><topic>ARIMA model</topic><topic>Atmospheric measurements</topic><topic>Pollution measurement</topic><topic>Predictive models</topic><topic>Short-term wind speed forecasting</topic><topic>Time series analysis</topic><topic>Wind forecasting</topic><topic>Wind speed</topic><toplevel>online_resources</toplevel><creatorcontrib>Meng-Di Wang</creatorcontrib><creatorcontrib>Qi-Rong Qiu</creatorcontrib><creatorcontrib>Bing-Wei Cui</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 Electronic Library (IEL)</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>Meng-Di Wang</au><au>Qi-Rong Qiu</au><au>Bing-Wei Cui</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Short-term wind speed forecasting combined time series method and arch model</atitle><btitle>2012 International Conference on Machine Learning and Cybernetics</btitle><stitle>ICMLC</stitle><date>2012-07</date><risdate>2012</risdate><volume>3</volume><spage>924</spage><epage>927</epage><pages>924-927</pages><issn>2160-133X</issn><isbn>1467314846</isbn><isbn>9781467314848</isbn><eisbn>9781467314879</eisbn><eisbn>1467314870</eisbn><eisbn>9781467314862</eisbn><eisbn>1467314862</eisbn><abstract>In order to improve the accuracy of the wind speed forecasting in the wind farm, this paper presents an ARIMA-ARCH model, which considers the heteroscedastic effect between the fluctuation of wind speed and the characteristics of the change of wind speed, to forecast the wind speed. First of all, the ARIMA model for the wind speed time series is built by SPSS. After that, the high lag order ARCH effect is found in the residual of the ARIMA model by Lagrange multiplier (LM) test. At last, the GARCH model is built for simulating the residual series and thus to construct the ARIMA-ARCH model. Numerical experiments demonstrate the superiority of the proposed method when comparing with the traditional ARIMA model.</abstract><pub>IEEE</pub><doi>10.1109/ICMLC.2012.6359477</doi><tpages>4</tpages></addata></record> |
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| identifier | ISSN: 2160-133X |
| ispartof | 2012 International Conference on Machine Learning and Cybernetics, 2012, Vol.3, p.924-927 |
| issn | 2160-133X |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Abstracts ARCH model ARIMA model Atmospheric measurements Pollution measurement Predictive models Short-term wind speed forecasting Time series analysis Wind forecasting Wind speed |
| title | Short-term wind speed forecasting combined time series method and arch model |
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