A solution method of unit commitment by artificial neural networks
The authors explore the possibility of applying the Hopfield neural network to combinatorial optimization problems in power systems, in particular to unit commitment. A large number of inequality constraints included in unit commitment can be handled by dedicated neural networks. As an exact mapping...
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| Veröffentlicht in: | IEEE transactions on power systems 1992-08, Vol.7 (3), p.974-981 |
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| container_end_page | 981 |
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| container_title | IEEE transactions on power systems |
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| creator | Sasaki, H. Watanabe, M. Kubokawa, J. Yorino, N. Yokoyama, R. |
| description | The authors explore the possibility of applying the Hopfield neural network to combinatorial optimization problems in power systems, in particular to unit commitment. A large number of inequality constraints included in unit commitment can be handled by dedicated neural networks. As an exact mapping of the problem onto the neural network is impossible with the state of the art, a two-step solution method was developed. First, generators to be stored up at each period are determined by the network, and then their outputs are adjusted by a conventional algorithm. The proposed neural network could solve a large-scale unit commitment problem with 30 generators over 24 periods, and results obtained were very encouraging.< > |
| doi_str_mv | 10.1109/59.207310 |
| format | Article |
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A large number of inequality constraints included in unit commitment can be handled by dedicated neural networks. As an exact mapping of the problem onto the neural network is impossible with the state of the art, a two-step solution method was developed. First, generators to be stored up at each period are determined by the network, and then their outputs are adjusted by a conventional algorithm. The proposed neural network could solve a large-scale unit commitment problem with 30 generators over 24 periods, and results obtained were very encouraging.< ></description><subject>990200 -- Mathematics & Computers</subject><subject>ALGORITHMS</subject><subject>ARTIFICIAL INTELLIGENCE</subject><subject>Artificial neural networks</subject><subject>Biological neural networks</subject><subject>CONSTRAINTS</subject><subject>GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE</subject><subject>Hopfield neural networks</subject><subject>Linear programming</subject><subject>MAPPING</subject><subject>MATHEMATICAL LOGIC 240100 -- Power Systems-- (1990-)</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>OPTIMIZATION</subject><subject>Power engineering and energy</subject><subject>Power system interconnection</subject><subject>Power system planning</subject><subject>POWER SYSTEMS</subject><subject>POWER TRANSMISSION AND DISTRIBUTION</subject><issn>0885-8950</issn><issn>1558-0679</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1992</creationdate><recordtype>article</recordtype><recordid>eNqF0L1PwzAQBXALgUQpDKxMEQMSQ8rZiWNnLBVfUiUWmC3HuaiGJC62I9T_ntBUrExveD89nY6QSwoLSqG84-WCgcgoHJEZ5VymUIjymMxASp7KksMpOQvhAwCKsZiR-2USXDtE6_qkw7hxdeKaZOhtTIzrOhs77GNS7RLto22ssbpNehz8PuK385_hnJw0ug14ccg5eX98eFs9p-vXp5fVcp2aHGhMMSsziaxmHIE1vOYUQWhT18zoLAddUSloIWWV18IYFCLPmBElz4WoxkZmc3I97boQrQrGRjQb4_oeTVSCScpYPqKbCW29-xowRNXZYLBtdY9uCIpJLhiMp_wLuSwg2y_eTtB4F4LHRm297bTfKQrq9-eKl2r6-WivJmsR8c8dyh8Le3qN</recordid><startdate>19920801</startdate><enddate>19920801</enddate><creator>Sasaki, H.</creator><creator>Watanabe, M.</creator><creator>Kubokawa, J.</creator><creator>Yorino, N.</creator><creator>Yokoyama, R.</creator><general>IEEE</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>7SP</scope><scope>OTOTI</scope></search><sort><creationdate>19920801</creationdate><title>A solution method of unit commitment by artificial neural networks</title><author>Sasaki, H. ; Watanabe, M. ; Kubokawa, J. ; Yorino, N. ; Yokoyama, R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c401t-e3938e2d25e02f5d51e07acdd2ca340ab1871688b4d7cce77432c795477b87183</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1992</creationdate><topic>990200 -- Mathematics & Computers</topic><topic>ALGORITHMS</topic><topic>ARTIFICIAL INTELLIGENCE</topic><topic>Artificial neural networks</topic><topic>Biological neural networks</topic><topic>CONSTRAINTS</topic><topic>GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE</topic><topic>Hopfield neural networks</topic><topic>Linear programming</topic><topic>MAPPING</topic><topic>MATHEMATICAL LOGIC 240100 -- Power Systems-- (1990-)</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>OPTIMIZATION</topic><topic>Power engineering and energy</topic><topic>Power system interconnection</topic><topic>Power system planning</topic><topic>POWER SYSTEMS</topic><topic>POWER TRANSMISSION AND DISTRIBUTION</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sasaki, H.</creatorcontrib><creatorcontrib>Watanabe, M.</creatorcontrib><creatorcontrib>Kubokawa, J.</creatorcontrib><creatorcontrib>Yorino, N.</creatorcontrib><creatorcontrib>Yokoyama, R.</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology 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><collection>Electronics & Communications Abstracts</collection><collection>OSTI.GOV</collection><jtitle>IEEE transactions on power systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Sasaki, H.</au><au>Watanabe, M.</au><au>Kubokawa, J.</au><au>Yorino, N.</au><au>Yokoyama, R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A solution method of unit commitment by artificial neural networks</atitle><jtitle>IEEE transactions on power systems</jtitle><stitle>TPWRS</stitle><date>1992-08-01</date><risdate>1992</risdate><volume>7</volume><issue>3</issue><spage>974</spage><epage>981</epage><pages>974-981</pages><issn>0885-8950</issn><eissn>1558-0679</eissn><coden>ITPSEG</coden><abstract>The authors explore the possibility of applying the Hopfield neural network to combinatorial optimization problems in power systems, in particular to unit commitment. A large number of inequality constraints included in unit commitment can be handled by dedicated neural networks. As an exact mapping of the problem onto the neural network is impossible with the state of the art, a two-step solution method was developed. First, generators to be stored up at each period are determined by the network, and then their outputs are adjusted by a conventional algorithm. The proposed neural network could solve a large-scale unit commitment problem with 30 generators over 24 periods, and results obtained were very encouraging.< ></abstract><cop>United States</cop><pub>IEEE</pub><doi>10.1109/59.207310</doi><tpages>8</tpages></addata></record> |
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| ispartof | IEEE transactions on power systems, 1992-08, Vol.7 (3), p.974-981 |
| issn | 0885-8950 1558-0679 |
| language | eng |
| recordid | cdi_osti_scitechconnect_7281224 |
| source | IEEE Electronic Library (IEL) |
| subjects | 990200 -- Mathematics & Computers ALGORITHMS ARTIFICIAL INTELLIGENCE Artificial neural networks Biological neural networks CONSTRAINTS GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE Hopfield neural networks Linear programming MAPPING MATHEMATICAL LOGIC 240100 -- Power Systems-- (1990-) Neural networks Neurons OPTIMIZATION Power engineering and energy Power system interconnection Power system planning POWER SYSTEMS POWER TRANSMISSION AND DISTRIBUTION |
| title | A solution method of unit commitment by artificial neural networks |
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