Repository logo
 
Publication

Scenarios Generation for Multi-Agent simulation of Electricity Markets based on Intelligent Data Analysis

2013-04Conference object Restricted access
Simple item page
dc.contributor.authorSantos, Gabriel
dc.contributor.authorPraça, Isabel
dc.contributor.authorPinto, Tiago
dc.contributor.authorRamos, Sérgio
dc.contributor.authorVale, Zita
dc.date.accessioned2015-05-04T10:11:28Z
dc.date.available2015-05-04T10:11:28Z
dc.date.issued2013-04
dc.description.abstractThis document presents a tool able to automatically gather data provided by real energy markets and to generate scenarios, capture and improve market players’ profiles and strategies by using knowledge discovery processes in databases supported by artificial intelligence techniques, data mining algorithms and machine learning methods. It provides the means for generating scenarios with different dimensions and characteristics, ensuring the representation of real and adapted markets, and their participating entities. The scenarios generator module enhances the MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) simulator, endowing a more effective tool for decision support. The achievements from the implementation of the proposed module enables researchers and electricity markets’ participating entities to analyze data, create real scenarios and make experiments with them. On the other hand, applying knowledge discovery techniques to real data also allows the improvement of MASCEM agents’ profiles and strategies resulting in a better representation of real market players’ behavior. This work aims to improve the comprehension of electricity markets and the interactions among the involved entities through adequate multi-agent simulation.por
dc.identifier.doi10.1109/IA.2013.6595183
dc.identifier.urihttp://hdl.handle.net/10400.22/5880
dc.language.isoengpor
dc.publisherIEEEpor
dc.relation.ispartofseriesIntelligent Agent (IA);2013
dc.relation.publisherversionieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6595183&queryText%3DScenarios+Generation+for+Multi-Agent+simulation+of+Electricity+Markets+based+on+Intelligent+Data+Analysispor
dc.subjectElectricity Marketspor
dc.subjectKnowledge Discovery in Databasespor
dc.subjectMachine Learningpor
dc.subjectMulti-Agent Simulatorspor
dc.subjectReal Electricity Marketspor
dc.subjectScenarios Generationpor
dc.titleScenarios Generation for Multi-Agent simulation of Electricity Markets based on Intelligent Data Analysispor
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceSingaporepor
oaire.citation.endPage12por
oaire.citation.startPage5por
oaire.citation.titleIEEE Symposium on Evolving and Adaptive Systems 2013 (EIAS) at the IEEE SSCI 2013 (IEEE Symposium Series on Computational Intelligence)por
person.familyNamePraça
person.familyNamePinto
person.familyNameCarvalho Ramos
person.familyNameVale
person.givenNameIsabel
person.givenNameTiago
person.givenNameSérgio Filipe
person.givenNameZita
person.identifier299522
person.identifierR-000-T7J
person.identifier632184
person.identifier.ciencia-idC710-4218-1BFF
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-id6D1F-C495-6660
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0002-2519-9859
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0002-1120-5656
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridK-8430-2014
person.identifier.ridT-2245-2018
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id22734900800
person.identifier.scopus-author-id35219107600
person.identifier.scopus-author-id7004115775
rcaap.rightsclosedAccesspor
rcaap.typeconferenceObjectpor
relation.isAuthorOfPublicationee4ecacd-c6c6-41e8-bca1-21a60ff05f50
relation.isAuthorOfPublication8d58ddc0-1023-47c0-a005-129d412ce98d
relation.isAuthorOfPublicationf01a54a0-e6c0-4cf3-afd8-5a664bbac7b4
relation.isAuthorOfPublicationff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isAuthorOfPublication.latestForDiscoveryee4ecacd-c6c6-41e8-bca1-21a60ff05f50

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
COM_GSantos_2013_GECAD.pdf
Size:
539.1 KB
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 – Comunicações em eventos científicos