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Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets

2013-08Conference object Restricted access
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dc.contributor.authorPinto, Tiago
dc.contributor.authorSousa, Tiago
dc.contributor.authorBarreira, Elisa
dc.contributor.authorPraça, Isabel
dc.contributor.authorVale, Zita
dc.date.accessioned2015-05-06T08:49:51Z
dc.date.available2015-05-06T08:49:51Z
dc.date.issued2013-08
dc.description.abstractThe restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players’ actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets’ players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets’ data, using MASCEM - a multi-agent electricity market simulator that simulates market players’ operation in the market.por
dc.identifier.doi10.1109/DEXA.2013.49
dc.identifier.urihttp://hdl.handle.net/10400.22/5937
dc.language.isoengpor
dc.publisherIEEEpor
dc.relation.ispartofseriesDEXA;2013
dc.relation.publisherversionhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6621368&queryText%3DMetalearner+based+on+Dynamic+Neural+Network+for+Strategic+Bidding+in+electricity+Marketspor
dc.subjectAdaptive Learningpor
dc.subjectArtificial Neural Networkpor
dc.subjectElectricity Marketspor
dc.subjectMulti-Agent Simulationpor
dc.subjectMetalearningpor
dc.titleMetalearner based on Dynamic Neural Network for Strategic Bidding in electricity Marketspor
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlacePraga, República Checapor
oaire.citation.titleSecond International Workshop on Intelligent Agent Technology, Power Systems and Energy Markets (IATEM 2013) at the 24th International Conference on Database and Expert Systems Applications (DEXA 2013)por
person.familyNamePinto
person.familyNamePraça
person.familyNameVale
person.givenNameTiago
person.givenNameIsabel
person.givenNameZita
person.identifierR-000-T7J
person.identifier299522
person.identifier632184
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-idC710-4218-1BFF
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0002-2519-9859
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridT-2245-2018
person.identifier.ridK-8430-2014
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id35219107600
person.identifier.scopus-author-id22734900800
person.identifier.scopus-author-id7004115775
rcaap.rightsopenAccesspor
rcaap.typeconferenceObjectpor
relation.isAuthorOfPublication8d58ddc0-1023-47c0-a005-129d412ce98d
relation.isAuthorOfPublicationee4ecacd-c6c6-41e8-bca1-21a60ff05f50
relation.isAuthorOfPublicationff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isAuthorOfPublication.latestForDiscoveryee4ecacd-c6c6-41e8-bca1-21a60ff05f50

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