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Adaptive learning in multiagent systems: a forecasting methodology based on error analysis

2012Book part Restricted access
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dc.contributor.authorSousa, Tiago
dc.contributor.authorPinto, Tiago
dc.contributor.authorVale, Zita
dc.contributor.authorPraça, Isabel
dc.contributor.authorMorais, H.
dc.date.accessioned2013-04-18T10:52:23Z
dc.date.available2013-04-18T10:52:23Z
dc.date.issued2012
dc.date.updated2013-04-12T11:28:27Z
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 is a multi-agent electricity market simu-lator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM pro-vides several dynamic strategies for agents’ behaviour. This paper presents a method that aims to provide market players strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses an auxiliary forecasting tool, e.g. an Artificial Neural Net-work, to predict the electricity market prices, and analyses its forecasting error patterns. Through the recognition of such patterns occurrence, the method predicts the expected error for the next forecast, and uses it to adapt the actual forecast. The goal is to approximate the forecast to the real value, reducing the forecasting error.por
dc.identifier.doi10.1007/978-3-642-28762-6_42pt_PT
dc.identifier.isbn978-3-642-28761-9
dc.identifier.isbn978-3-642-28762-6
dc.identifier.issn1867-5662
dc.identifier.urihttp://hdl.handle.net/10400.22/1395
dc.language.isoengpor
dc.publisherSpringer Berlin Heidelbergpor
dc.relation.ispartofseriesAdvances in Intelligent and Soft Computing; Vol. 156
dc.relation.publisherversionhttp://link.springer.com/chapter/10.1007/978-3-642-28762-6_42por
dc.subjectAdaptive learningpor
dc.subjectElectricity marketspor
dc.subjectError analysispor
dc.subjectForecasting methodspor
dc.subjectInformation theorypor
dc.subjectMultiagent systemspor
dc.titleAdaptive learning in multiagent systems: a forecasting methodology based on error analysispor
dc.typebook part
dspace.entity.typePublication
oaire.citation.endPage357por
oaire.citation.startPage349por
oaire.citation.titleHighlights on practical applications of agents and multi-agent systems. 10th International Conference on Practical Applications of Agents and Multi-Agent Systemspor
oaire.citation.volumeVol. 156
person.familyNamePinto
person.familyNameVale
person.familyNamePraça
person.givenNameTiago
person.givenNameZita
person.givenNameIsabel
person.identifierR-000-T7J
person.identifier632184
person.identifier299522
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.ciencia-idC710-4218-1BFF
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0002-4560-9544
person.identifier.orcid0000-0002-2519-9859
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
rcaap.rightsclosedAccesspor
rcaap.typebookPartpor
relation.isAuthorOfPublication8d58ddc0-1023-47c0-a005-129d412ce98d
relation.isAuthorOfPublicationff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isAuthorOfPublicationee4ecacd-c6c6-41e8-bca1-21a60ff05f50
relation.isAuthorOfPublication.latestForDiscoveryff1df02d-0c0f-4db1-bf7d-78863a99420b

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