Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks
[Display omitted] ► Three new methods are proposed to predict wind speed for the wind power system. ► The Wavelet Packet-BFGS is better than the Wavelet Packet-ARIMA-BFGS. ► The Wavelet Packet-BFGS is better than the Wavelet-BFGS. ► They are compared to the Neuro-Fuzzy, ANFIS, RBF neural network and...
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| Veröffentlicht in: | Applied energy 2013-07, Vol.107, p.191-208 |
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| container_title | Applied energy |
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| creator | Liu, Hui Tian, Hong-qi Pan, Di-fu Li, Yan-fei |
| description | [Display omitted]
► Three new methods are proposed to predict wind speed for the wind power system. ► The Wavelet Packet-BFGS is better than the Wavelet Packet-ARIMA-BFGS. ► The Wavelet Packet-BFGS is better than the Wavelet-BFGS. ► They are compared to the Neuro-Fuzzy, ANFIS, RBF neural network and PM.
Wind speed forecasting is important for the security of wind power integration. Based on the theories of wavelet, wavelet packet, time series analysis and artificial neural networks, three hybrid models [Wavelet Packet-BFGS, Wavelet Packet-ARIMA-BFGS and Wavelet-BFGS] are proposed to predict the wind speed. The presented models are compared with some other classical wind speed forecasting methods including Neuro-Fuzzy, ANFIS (Adaptive Neuro-Fuzzy Inference Systems), Wavelet Packet-RBF (Radial Basis Function) and PM (Persistent Model). The results of three experimental cases show that: (1) the proposed three hybrid models have satisfactory performance in the wind speed predictions, and (2) the Wavelet Packet-ANN model is the best among them. |
| doi_str_mv | 10.1016/j.apenergy.2013.02.002 |
| format | Article |
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► Three new methods are proposed to predict wind speed for the wind power system. ► The Wavelet Packet-BFGS is better than the Wavelet Packet-ARIMA-BFGS. ► The Wavelet Packet-BFGS is better than the Wavelet-BFGS. ► They are compared to the Neuro-Fuzzy, ANFIS, RBF neural network and PM.
Wind speed forecasting is important for the security of wind power integration. Based on the theories of wavelet, wavelet packet, time series analysis and artificial neural networks, three hybrid models [Wavelet Packet-BFGS, Wavelet Packet-ARIMA-BFGS and Wavelet-BFGS] are proposed to predict the wind speed. The presented models are compared with some other classical wind speed forecasting methods including Neuro-Fuzzy, ANFIS (Adaptive Neuro-Fuzzy Inference Systems), Wavelet Packet-RBF (Radial Basis Function) and PM (Persistent Model). The results of three experimental cases show that: (1) the proposed three hybrid models have satisfactory performance in the wind speed predictions, and (2) the Wavelet Packet-ANN model is the best among them.</description><identifier>ISSN: 0306-2619</identifier><identifier>EISSN: 1872-9118</identifier><identifier>DOI: 10.1016/j.apenergy.2013.02.002</identifier><identifier>CODEN: APENDX</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>ANN ; Applied sciences ; ARIMA ; Artificial neural networks ; Energy ; Exact sciences and technology ; Forecasting ; Fuzzy logic ; Hybrid model ; neural networks ; Packets (communication) ; prediction ; Signal decomposition ; Time series ; Time series analysis ; Wavelet ; wind power ; Wind speed ; Wind speed forecasting ; Wind speed predictions</subject><ispartof>Applied energy, 2013-07, Vol.107, p.191-208</ispartof><rights>2013 Elsevier Ltd</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c432t-9387e58715a6543120326342ceece10c4d6accc192406513222e2ddd3c34b7443</citedby><cites>FETCH-LOGICAL-c432t-9387e58715a6543120326342ceece10c4d6accc192406513222e2ddd3c34b7443</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0306261913001104$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3536,27903,27904,65309</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=27307209$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Liu, Hui</creatorcontrib><creatorcontrib>Tian, Hong-qi</creatorcontrib><creatorcontrib>Pan, Di-fu</creatorcontrib><creatorcontrib>Li, Yan-fei</creatorcontrib><title>Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks</title><title>Applied energy</title><description>[Display omitted]
► Three new methods are proposed to predict wind speed for the wind power system. ► The Wavelet Packet-BFGS is better than the Wavelet Packet-ARIMA-BFGS. ► The Wavelet Packet-BFGS is better than the Wavelet-BFGS. ► They are compared to the Neuro-Fuzzy, ANFIS, RBF neural network and PM.
Wind speed forecasting is important for the security of wind power integration. Based on the theories of wavelet, wavelet packet, time series analysis and artificial neural networks, three hybrid models [Wavelet Packet-BFGS, Wavelet Packet-ARIMA-BFGS and Wavelet-BFGS] are proposed to predict the wind speed. The presented models are compared with some other classical wind speed forecasting methods including Neuro-Fuzzy, ANFIS (Adaptive Neuro-Fuzzy Inference Systems), Wavelet Packet-RBF (Radial Basis Function) and PM (Persistent Model). The results of three experimental cases show that: (1) the proposed three hybrid models have satisfactory performance in the wind speed predictions, and (2) the Wavelet Packet-ANN model is the best among them.</description><subject>ANN</subject><subject>Applied sciences</subject><subject>ARIMA</subject><subject>Artificial neural networks</subject><subject>Energy</subject><subject>Exact sciences and technology</subject><subject>Forecasting</subject><subject>Fuzzy logic</subject><subject>Hybrid model</subject><subject>neural networks</subject><subject>Packets (communication)</subject><subject>prediction</subject><subject>Signal decomposition</subject><subject>Time series</subject><subject>Time series analysis</subject><subject>Wavelet</subject><subject>wind power</subject><subject>Wind speed</subject><subject>Wind speed forecasting</subject><subject>Wind speed predictions</subject><issn>0306-2619</issn><issn>1872-9118</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNqFkcFu1DAQhi0EUpfCKxRfkDiQYI8TJ7lRVS1FquDQ9mwZe7LybjYOnmxXfXsctu21p7Hl7x-P_TF2JkUphdTfNqWdcMS0fixBSFUKKIWAN2wl2waKTsr2LVsJJXQBWnYn7D3RRmRCglix7VVM6CzNYVzzXfQ4EO9j4ocwek4Toud7Ws4O9gEHnL8-L_hk3XbZz2GHnDAFJG5z6DzNoQ8u2IH_wn36X-ZDTFv6wN71diD8-FRP2f3V5d3FdXHz-8fPi_ObwlUK5qJTbYN128ja6rpSeUwFWlXgEB1K4SqvrXNOdlAJXUsFAAjee-VU9aepKnXKvhz7Tin-3SPNZhfI4TDYEeOejFxSTQ1t-zpaC626RmvIqD6iLkWihL2ZUtjZ9GikMIsIszHPIswiwggw-Ztz8PPTHZacHfpkRxfoJQ2NEg2ILnOfjlxvo7HrlJn729xIZ1n5lZ3MxPcjkS3hQ8BkyAUcHfqQJc7Gx_DaMP8Abdaqeg</recordid><startdate>20130701</startdate><enddate>20130701</enddate><creator>Liu, Hui</creator><creator>Tian, Hong-qi</creator><creator>Pan, Di-fu</creator><creator>Li, Yan-fei</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>FBQ</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>7SC</scope><scope>7SP</scope><scope>7TA</scope><scope>F28</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20130701</creationdate><title>Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks</title><author>Liu, Hui ; Tian, Hong-qi ; Pan, Di-fu ; Li, Yan-fei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c432t-9387e58715a6543120326342ceece10c4d6accc192406513222e2ddd3c34b7443</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>ANN</topic><topic>Applied sciences</topic><topic>ARIMA</topic><topic>Artificial neural networks</topic><topic>Energy</topic><topic>Exact sciences and technology</topic><topic>Forecasting</topic><topic>Fuzzy logic</topic><topic>Hybrid model</topic><topic>neural networks</topic><topic>Packets (communication)</topic><topic>prediction</topic><topic>Signal decomposition</topic><topic>Time series</topic><topic>Time series analysis</topic><topic>Wavelet</topic><topic>wind power</topic><topic>Wind speed</topic><topic>Wind speed forecasting</topic><topic>Wind speed predictions</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Hui</creatorcontrib><creatorcontrib>Tian, Hong-qi</creatorcontrib><creatorcontrib>Pan, Di-fu</creatorcontrib><creatorcontrib>Li, Yan-fei</creatorcontrib><collection>AGRIS</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Materials Business File</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Applied energy</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Hui</au><au>Tian, Hong-qi</au><au>Pan, Di-fu</au><au>Li, Yan-fei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks</atitle><jtitle>Applied energy</jtitle><date>2013-07-01</date><risdate>2013</risdate><volume>107</volume><spage>191</spage><epage>208</epage><pages>191-208</pages><issn>0306-2619</issn><eissn>1872-9118</eissn><coden>APENDX</coden><abstract>[Display omitted]
► Three new methods are proposed to predict wind speed for the wind power system. ► The Wavelet Packet-BFGS is better than the Wavelet Packet-ARIMA-BFGS. ► The Wavelet Packet-BFGS is better than the Wavelet-BFGS. ► They are compared to the Neuro-Fuzzy, ANFIS, RBF neural network and PM.
Wind speed forecasting is important for the security of wind power integration. Based on the theories of wavelet, wavelet packet, time series analysis and artificial neural networks, three hybrid models [Wavelet Packet-BFGS, Wavelet Packet-ARIMA-BFGS and Wavelet-BFGS] are proposed to predict the wind speed. The presented models are compared with some other classical wind speed forecasting methods including Neuro-Fuzzy, ANFIS (Adaptive Neuro-Fuzzy Inference Systems), Wavelet Packet-RBF (Radial Basis Function) and PM (Persistent Model). The results of three experimental cases show that: (1) the proposed three hybrid models have satisfactory performance in the wind speed predictions, and (2) the Wavelet Packet-ANN model is the best among them.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.apenergy.2013.02.002</doi><tpages>18</tpages></addata></record> |
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| source | Elsevier ScienceDirect Journals |
| subjects | ANN Applied sciences ARIMA Artificial neural networks Energy Exact sciences and technology Forecasting Fuzzy logic Hybrid model neural networks Packets (communication) prediction Signal decomposition Time series Time series analysis Wavelet wind power Wind speed Wind speed forecasting Wind speed predictions |
| title | Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks |
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