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Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

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dc.contributor.authorSousa, Tiago
dc.contributor.authorPinto, Tiago
dc.contributor.authorPraça, Isabel
dc.contributor.authorVale, Zita
dc.contributor.authorMorais, Hugo
dc.date.accessioned2015-05-04T16:37:29Z
dc.date.available2015-05-04T16:37:29Z
dc.date.issued2014
dc.description.abstractThis paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.por
dc.identifier.doi10.1007/978-3-319-07593-8_18
dc.identifier.urihttp://hdl.handle.net/10400.22/5904
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherSpringerpor
dc.relation.ispartofseriesAdvances in Intelligent Systems and Computing;Vol. 290
dc.relation.publisherversionhttp://link.springer.com/chapter/10.1007/978-3-319-07593-8_18por
dc.subjectReinforcement Learningpor
dc.subjectBayesian theorempor
dc.subjectMASCEMpor
dc.subjectALBidSpor
dc.titleReinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Supportpor
dc.typebook part
dspace.entity.typePublication
oaire.citation.endPage148por
oaire.citation.startPage141por
oaire.citation.title11th International Conference in Distributed Computing and Artificial Intelligencepor
oaire.citation.volume290por
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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