Repository logo
 
Publication

Adaptive learning in agents behaviour: A framework for electricity markets simulation

2014Journal article Open access
Simple item page
dc.contributor.authorPinto, Tiago
dc.contributor.authorVale, Zita
dc.contributor.authorSousa, Tiago
dc.contributor.authorPraça, Isabel
dc.contributor.authorSantos, Gabriel
dc.contributor.authorMorais, Hugo
dc.date.accessioned2014-12-09T13:01:59Z
dc.date.available2014-12-09T13:01:59Z
dc.date.issued2014
dc.description.abstractElectricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.por
dc.identifier.doi10.3233/ICA-140477
dc.identifier.doi1069-2509
dc.identifier.urihttp://hdl.handle.net/10400.22/5244
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherIOS Presspor
dc.relation.ispartofseriesIntegrated Computer-Aided Engineering;Vol.21, nº 4
dc.relation.publisherversionhttp://iospress.metapress.com/content/x2282034073838q3/?p=632a8354ab3441fa88ab2782e81846bc&pi=6por
dc.subjectAdaptive learningpor
dc.subjectArtificial Intelligencepor
dc.subjectElectricity marketspor
dc.subjectMachine learningpor
dc.subjectMultiagent simulationpor
dc.titleAdaptive learning in agents behaviour: A framework for electricity markets simulationpor
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage415por
oaire.citation.startPage399por
oaire.citation.titleIntegrated Computer-Aided Engineeringpor
person.familyNamePinto
person.familyNameVale
person.familyNamePraça
person.familyNameMorais
person.givenNameTiago
person.givenNameZita
person.givenNameIsabel
person.givenNameHugo
person.identifierR-000-T7J
person.identifier632184
person.identifier299522
person.identifier80878
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.ciencia-idC710-4218-1BFF
person.identifier.ciencia-id2010-D878-271B
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0002-4560-9544
person.identifier.orcid0000-0002-2519-9859
person.identifier.orcid0000-0001-5906-4744
person.identifier.ridT-2245-2018
person.identifier.ridA-5824-2012
person.identifier.ridK-8430-2014
person.identifier.scopus-author-id35219107600
person.identifier.scopus-author-id7004115775
person.identifier.scopus-author-id22734900800
person.identifier.scopus-author-id21834170800
rcaap.rightsopenAccesspor
rcaap.typearticlepor
relation.isAuthorOfPublication8d58ddc0-1023-47c0-a005-129d412ce98d
relation.isAuthorOfPublicationff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isAuthorOfPublicationee4ecacd-c6c6-41e8-bca1-21a60ff05f50
relation.isAuthorOfPublicationb159f8c9-5ee1-444e-b890-81242ee0738e
relation.isAuthorOfPublication.latestForDiscoveryff1df02d-0c0f-4db1-bf7d-78863a99420b

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ART2_Vale_2014_GECAD.pdf
Size:
1.25 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

ISEP – GECAD – Artigos