Using AWPSO to Solve the Data Scarcity Problem in Wind Speed Prediction by Artificial Neural Networks
A new strategy in wind speed prediction based on adaptive weighted particle swarm optimization combined with artificial neural networks was proposed. Regarding the data gathering, sometimes it is difficult to provide the neural network with sufficient data to be trained efficiently. In order to solv...
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| creator | Fesharaki, M Shafiabady, N Fesharaki, M A Ahmadi, S |
| description | A new strategy in wind speed prediction based on adaptive weighted particle swarm optimization combined with artificial neural networks was proposed. Regarding the data gathering, sometimes it is difficult to provide the neural network with sufficient data to be trained efficiently. In order to solve this problem Adaptive weighed particle swarm optimization is used to increase the data the produced data is fed to a multilayered feed forward neural network to predict the future wind speed. This method has lead to good estimated wind speed accuracy and good prediction performance. |
| doi_str_mv | 10.1109/AICI.2010.251 |
| format | Conference Proceeding |
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Regarding the data gathering, sometimes it is difficult to provide the neural network with sufficient data to be trained efficiently. In order to solve this problem Adaptive weighed particle swarm optimization is used to increase the data the produced data is fed to a multilayered feed forward neural network to predict the future wind speed. This method has lead to good estimated wind speed accuracy and good prediction performance.</description><subject>Adaptive Weighted Particle Swarm Optimization</subject><subject>Artificial neural networks</subject><subject>Data Scarcity</subject><subject>Multilayered Feed Forward Neural Network</subject><subject>Neurons</subject><subject>Particle swarm optimization</subject><subject>Prediction</subject><subject>Simulation</subject><subject>Wind power generation</subject><subject>Wind speed</subject><subject>Wind turbines</subject><isbn>1424484324</isbn><isbn>9781424484324</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotjEtPwkAURicxJAqydOXm_gFwXndslw0-aEKEpBiWZDq91dHSkumo4d9bH9_m5DuLw9iV4HMheHqT5Yt8LvlwJYozNhZaap1oJfWIjX98qm4FynM27fs3PgwTbnRywei59-0LZLtNsYbYQdE1nwTxleDORguFs8H5eIJN6MqGDuBb2Pm2guJIVA2WKu-i71ooT5CF6GvvvG3giT7CL-JXF977SzaqbdPT9J8Ttn243y6Ws9X6MV9kq5lPeZwJWRMaY6yRZCpjk6RWiLLESiGRcyVKq20y0JRllSpSHGuRWilVWkvH1YRd_2U9Ee2PwR9sOO3RIAql1TcOg1Uc</recordid><startdate>201010</startdate><enddate>201010</enddate><creator>Fesharaki, M</creator><creator>Shafiabady, N</creator><creator>Fesharaki, M A</creator><creator>Ahmadi, S</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201010</creationdate><title>Using AWPSO to Solve the Data Scarcity Problem in Wind Speed Prediction by Artificial Neural Networks</title><author>Fesharaki, M ; Shafiabady, N ; Fesharaki, M A ; Ahmadi, S</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-12fe5666a62e6d6a88f3552b5d35eeccb52a4a8cb56bbd93e305f19a2239f2c03</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Adaptive Weighted Particle Swarm Optimization</topic><topic>Artificial neural networks</topic><topic>Data Scarcity</topic><topic>Multilayered Feed Forward Neural Network</topic><topic>Neurons</topic><topic>Particle swarm optimization</topic><topic>Prediction</topic><topic>Simulation</topic><topic>Wind power generation</topic><topic>Wind speed</topic><topic>Wind turbines</topic><toplevel>online_resources</toplevel><creatorcontrib>Fesharaki, M</creatorcontrib><creatorcontrib>Shafiabady, N</creatorcontrib><creatorcontrib>Fesharaki, M A</creatorcontrib><creatorcontrib>Ahmadi, S</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>Fesharaki, M</au><au>Shafiabady, N</au><au>Fesharaki, M A</au><au>Ahmadi, S</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Using AWPSO to Solve the Data Scarcity Problem in Wind Speed Prediction by Artificial Neural Networks</atitle><btitle>2010 International Conference on Artificial Intelligence and Computational Intelligence</btitle><stitle>AICI</stitle><date>2010-10</date><risdate>2010</risdate><volume>3</volume><spage>49</spage><epage>52</epage><pages>49-52</pages><isbn>1424484324</isbn><isbn>9781424484324</isbn><abstract>A new strategy in wind speed prediction based on adaptive weighted particle swarm optimization combined with artificial neural networks was proposed. Regarding the data gathering, sometimes it is difficult to provide the neural network with sufficient data to be trained efficiently. In order to solve this problem Adaptive weighed particle swarm optimization is used to increase the data the produced data is fed to a multilayered feed forward neural network to predict the future wind speed. This method has lead to good estimated wind speed accuracy and good prediction performance.</abstract><pub>IEEE</pub><doi>10.1109/AICI.2010.251</doi><tpages>4</tpages></addata></record> |
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| subjects | Adaptive Weighted Particle Swarm Optimization Artificial neural networks Data Scarcity Multilayered Feed Forward Neural Network Neurons Particle swarm optimization Prediction Simulation Wind power generation Wind speed Wind turbines |
| title | Using AWPSO to Solve the Data Scarcity Problem in Wind Speed Prediction by Artificial Neural Networks |
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