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Strategic Bidding for Electricity Markets Negotiation Using Support Vector Machines

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dc.contributor.authorPereira, Rafael
dc.contributor.authorSousa, Tiago
dc.contributor.authorPinto, Tiago
dc.contributor.authorPraça, Isabel
dc.contributor.authorVale, Zita
dc.contributor.authorMorais, Hugo
dc.date.accessioned2015-05-04T16:42:23Z
dc.date.available2015-05-04T16:42:23Z
dc.date.issued2014
dc.description.abstractEnergy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which simulates the electricity markets environment. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated by being included in ALBidS and then compared with the application of an Artificial Neural Network, originating promising results. The proposed approach is tested and validated using real electricity markets data from MIBEL - Iberian market operator.por
dc.identifier.doi10.1007/978-3-319-07476-4_2
dc.identifier.urihttp://hdl.handle.net/10400.22/5906
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherSpringerpor
dc.relation.ispartofseriesAdvances in Intelligent Systems and Computing;Vol. 293
dc.relation.publisherversionhttp://link.springer.com/chapter/10.1007/978-3-319-07476-4_2por
dc.subjectMASCEMpor
dc.subjectALBidSpor
dc.subjectMulti-agent based simulationpor
dc.titleStrategic Bidding for Electricity Markets Negotiation Using Support Vector Machinespor
dc.typebook part
dspace.entity.typePublication
oaire.citation.endPage17por
oaire.citation.startPage9por
oaire.citation.titleAdvances in Intelligent Systems and Computingpor
oaire.citation.volume293por
person.familyNamePinto
person.familyNamePraça
person.familyNameVale
person.familyNameMorais
person.givenNameTiago
person.givenNameIsabel
person.givenNameZita
person.givenNameHugo
person.identifierR-000-T7J
person.identifier299522
person.identifier632184
person.identifier80878
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-idC710-4218-1BFF
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.ciencia-id2010-D878-271B
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0002-2519-9859
person.identifier.orcid0000-0002-4560-9544
person.identifier.orcid0000-0001-5906-4744
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
person.identifier.scopus-author-id21834170800
rcaap.rightsclosedAccesspor
rcaap.typebookPartpor
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