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Dynamic Fuzzy Clustering Method for Decision Support in Electricity Markets Negotiation

2016Journal article Open access
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dc.contributor.authorFaia, Ricardo
dc.contributor.authorPinto, Tiago
dc.contributor.authorVale, Zita
dc.date.accessioned2017-01-25T10:47:41Z
dc.date.available2017-01-25T10:47:41Z
dc.date.issued2016
dc.description.abstractArtificial Intelligence (AI) methods contribute to the construction of systems where there is a need to automate the tasks. They are typically used for problems that have a large response time, or when a mathematical method cannot be used to solve the problem. However, the application of AI brings an added complexity to the development of such applications. AI has been frequently applied in the power systems field, namely in Electricity Markets (EM). In this area, AI applications are essentially used to forecast / estimate the prices of electricity or to search for the best opportunity to sell the product. This paper proposes a clustering methodology that is combined with fuzzy logic in order to perform the estimation of EM prices. The proposed method is based on the application of a clustering methodology that groups historic energy contracts according to their prices’ similarity. The optimal number of groups is automatically calculated taking into account the preference for the balance between the estimation error and the number of groups. The centroids of each cluster are used to define a dynamic fuzzy variable that approximates the tendency of contracts’ history. The resulting fuzzy variable allows estimating expected prices for contracts instantaneously and approximating missing values in the historic contracts.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.14201/ADCAIJ2016512336pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/9389
dc.language.isoengpt_PT
dc.publisherEdiciones Universidad de Salamancapt_PT
dc.relation.ispartofseriesADCAIJ;Vol. 5, Issue 1
dc.relation.publisherversionhttp://revistas.usal.es/index.php/2255-2863/article/view/ADCAIJ2016512336/0pt_PT
dc.subjectArtificial intelligencept_PT
dc.subjectClusteringpt_PT
dc.subjectElectricity marketspt_PT
dc.subjectFuzzy logicpt_PT
dc.titleDynamic Fuzzy Clustering Method for Decision Support in Electricity Markets Negotiationpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage35pt_PT
oaire.citation.issue1pt_PT
oaire.citation.startPage23pt_PT
oaire.citation.titleADCAIJ : Advances in Distributed Computing and Artificial Intelligence Journalpt_PT
oaire.citation.volume5pt_PT
person.familyNamePinto
person.familyNameVale
person.givenNameTiago
person.givenNameZita
person.identifierR-000-T7J
person.identifier632184
person.identifier.ciencia-id2414-9B03-C4BB
person.identifier.ciencia-id721B-B0EB-7141
person.identifier.orcid0000-0001-8248-080X
person.identifier.orcid0000-0002-4560-9544
person.identifier.ridT-2245-2018
person.identifier.ridA-5824-2012
person.identifier.scopus-author-id35219107600
person.identifier.scopus-author-id7004115775
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication8d58ddc0-1023-47c0-a005-129d412ce98d
relation.isAuthorOfPublicationff1df02d-0c0f-4db1-bf7d-78863a99420b
relation.isAuthorOfPublication.latestForDiscovery8d58ddc0-1023-47c0-a005-129d412ce98d

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