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Support Vector Machines for decision support in electricity markets' strategic bidding

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dc.contributor.authorPinto, Tiago
dc.contributor.authorSousa, Tiago M.
dc.contributor.authorPraça, Isabel
dc.contributor.authorVale, Zita
dc.contributor.authorMorais, Hugo
dc.date.accessioned2017-01-25T10:29:05Z
dc.date.embargo2117-02
dc.date.issued2015
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: Multi-Agent System for Competitive Electricity Markets (MASCEM), which simulates the electricity markets environment. MASCEM is integrated with Adaptive Learning Strategic Bidding System (ALBidS) 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 (ANN), originating promising results: an effective electricity market price forecast in a fast execution time. The proposed approach is tested and validated using real electricity markets data from MIBEL –Iberian market operator.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doihttp://dx.doi.org/10.1016/j.neucom.2015.03.102pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/9384
dc.language.isoengpt_PT
dc.publisherElsevierpt_PT
dc.relation.ispartofseriesNeurocomputing;Vol. 172
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0925231215010553pt_PT
dc.subjectArtificial Neural Networkspt_PT
dc.subjectDecision support systemspt_PT
dc.subjectElectricity marketspt_PT
dc.subjectMulti-agent simulationpt_PT
dc.subjectSupport Vector Machinespt_PT
dc.titleSupport Vector Machines for decision support in electricity markets' strategic biddingpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage445pt_PT
oaire.citation.startPage438pt_PT
oaire.citation.titleNeurocomputingpt_PT
oaire.citation.volume172pt_PT
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
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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.rightsclosedAccesspt_PT
rcaap.typearticlept_PT
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