Pareto Optimal Prediction Intervals of Electricity Price
This letter proposes a novel Pareto optimal prediction interval construction approach for electricity price combing extreme learning machine and non-dominated sorting genetic algorithm II (NSGA-II). The Pareto optimal prediction intervals are produced with respect to the formulated two objectives re...
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| Veröffentlicht in: | IEEE transactions on power systems 2017-01, Vol.32 (1), p.817-819 |
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| container_title | IEEE transactions on power systems |
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| creator | Wan, Can Niu, Ming Song, Yonghua Xu, Zhao |
| description | This letter proposes a novel Pareto optimal prediction interval construction approach for electricity price combing extreme learning machine and non-dominated sorting genetic algorithm II (NSGA-II). The Pareto optimal prediction intervals are produced with respect to the formulated two objectives reliability and sharpness. The effectiveness of proposed approach has been verified through the numerical studies on Australia electricity market data. |
| doi_str_mv | 10.1109/TPWRS.2016.2550867 |
| format | Article |
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The Pareto optimal prediction intervals are produced with respect to the formulated two objectives reliability and sharpness. The effectiveness of proposed approach has been verified through the numerical studies on Australia electricity market data.</description><identifier>ISSN: 0885-8950</identifier><identifier>EISSN: 1558-0679</identifier><identifier>DOI: 10.1109/TPWRS.2016.2550867</identifier><identifier>CODEN: ITPSEG</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Artificial neural networks ; Electricity price ; Electricity pricing ; Electricity supply industry ; extreme learning machine ; Forecasting ; Genetic algorithms ; Intervals ; Machine learning ; NSGA-II ; Pareto optimization ; Pareto optimum ; Power system reliability ; prediction intervals ; Probabilistic logic ; Reliability ; Sorting algorithms</subject><ispartof>IEEE transactions on power systems, 2017-01, Vol.32 (1), p.817-819</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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The Pareto optimal prediction intervals are produced with respect to the formulated two objectives reliability and sharpness. The effectiveness of proposed approach has been verified through the numerical studies on Australia electricity market data.</description><subject>Artificial neural networks</subject><subject>Electricity price</subject><subject>Electricity pricing</subject><subject>Electricity supply industry</subject><subject>extreme learning machine</subject><subject>Forecasting</subject><subject>Genetic algorithms</subject><subject>Intervals</subject><subject>Machine learning</subject><subject>NSGA-II</subject><subject>Pareto optimization</subject><subject>Pareto optimum</subject><subject>Power system reliability</subject><subject>prediction intervals</subject><subject>Probabilistic logic</subject><subject>Reliability</subject><subject>Sorting algorithms</subject><issn>0885-8950</issn><issn>1558-0679</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE1Lw0AQhhdRsFb_gF4CnhNnvzdHKbUWCg1a8bikmwlsiU3dbIX-e7e2eJrDvM98PITcUygohfJpVX2-vRcMqCqYlGCUviAjKqXJQenykozAGJmbUsI1uRmGDQCo1BgRU9UBY58td9F_1V1WBWy8i77fZvNtxPBTd0PWt9m0QxeDdz4eUsY7vCVXberh3bmOycfLdDV5zRfL2XzyvMgdFyLmJQeUWjAuXcMEYmka1EIw1XKkzGne1FSphvG1ZtoY4VjphBZOOWjZmms-Jo-nubvQf-9xiHbT78M2rbTUSMUTw0VKsVPKhX4YArZ2F9I_4WAp2KMh-2fIHg3Zs6EEPZwgj4j_QLrOCG34L4q_YO0</recordid><startdate>201701</startdate><enddate>201701</enddate><creator>Wan, Can</creator><creator>Niu, Ming</creator><creator>Song, Yonghua</creator><creator>Xu, Zhao</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0003-4480-7394</orcidid></search><sort><creationdate>201701</creationdate><title>Pareto Optimal Prediction Intervals of Electricity Price</title><author>Wan, Can ; Niu, Ming ; Song, Yonghua ; Xu, Zhao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c344t-930e574235cd24ee98de74426f3e12c73da166d23b727884c29c474c6c0f2b373</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Artificial neural networks</topic><topic>Electricity price</topic><topic>Electricity pricing</topic><topic>Electricity supply industry</topic><topic>extreme learning machine</topic><topic>Forecasting</topic><topic>Genetic algorithms</topic><topic>Intervals</topic><topic>Machine learning</topic><topic>NSGA-II</topic><topic>Pareto optimization</topic><topic>Pareto optimum</topic><topic>Power system reliability</topic><topic>prediction intervals</topic><topic>Probabilistic logic</topic><topic>Reliability</topic><topic>Sorting algorithms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wan, Can</creatorcontrib><creatorcontrib>Niu, Ming</creatorcontrib><creatorcontrib>Song, Yonghua</creatorcontrib><creatorcontrib>Xu, Zhao</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998–Present</collection><collection>IEEE Xplore</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on power systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Wan, Can</au><au>Niu, Ming</au><au>Song, Yonghua</au><au>Xu, Zhao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Pareto Optimal Prediction Intervals of Electricity Price</atitle><jtitle>IEEE transactions on power systems</jtitle><stitle>TPWRS</stitle><date>2017-01</date><risdate>2017</risdate><volume>32</volume><issue>1</issue><spage>817</spage><epage>819</epage><pages>817-819</pages><issn>0885-8950</issn><eissn>1558-0679</eissn><coden>ITPSEG</coden><abstract>This letter proposes a novel Pareto optimal prediction interval construction approach for electricity price combing extreme learning machine and non-dominated sorting genetic algorithm II (NSGA-II). 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| language | eng |
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| subjects | Artificial neural networks Electricity price Electricity pricing Electricity supply industry extreme learning machine Forecasting Genetic algorithms Intervals Machine learning NSGA-II Pareto optimization Pareto optimum Power system reliability prediction intervals Probabilistic logic Reliability Sorting algorithms |
| title | Pareto Optimal Prediction Intervals of Electricity Price |
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