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dc.contributor.authorValero Verdú, Sergio 
dc.contributor.authorGabaldón Marín, Antonio 
dc.contributor.authorEncinas Redondo, Nuria 
dc.contributor.authorOrtiz García, Mario 
dc.contributor.authorGarcía Franco, Francisco J. 
dc.contributor.authorSenabre Blanes, Carolina 
dc.contributor.authorCárcel, Juan Antonio 
dc.contributor.authorMolina García, Ángel 
dc.date.accessioned2009-10-02T06:50:51Z
dc.date.available2009-10-02T06:50:51Z
dc.date.issued2006-02
dc.identifier.citationVALERO, Sergio, GABALDÓN, Antonio, ENCINAS, Nuria, ORTIZ, Mario, GARCÍA FRANCO, Francisco, SENABRE, Carolina, CÁRCEL, Juan Antonio, MOLINA, Ángel. Development of new tools to promote a more effective consumer participation in short-term electricity markets. En: International ICSC Symposium on Artificial Intelligence in Energy Systems and Power (1º: 2006: Madeira, Portugal). International ICSC Symposium on Artificial Intelligence in Energy Systems and Power. AIESP 06. Madeira, Portugal, Febrero 7-10 2006. Madeira, Portugal: ICSC Academic Press Canada. 2006. 6p. ISBN 3-906454-36-3es
dc.identifier.isbn3-906454-36-3
dc.description.abstractThis paper summarizes the research work performed to show the capability of a combination of tools based on Self-Organizing Maps (SOM) and Physically Based Load Models (PBLM) to classify and extract pat-terns from distributor, aggregator and customer electrical demand databases (the objective known as data mining). This approach basically uses low cost information avail-able for almost all supply side agents: historic load curves of several kinds of customers. The first objective is to find a correlation between demand and the evolution of energy prices in short-term energy markets. A SOM was trained that should allow to select the most suitable customer clusters whose demand modification would benefit cus-tomer and supply-side agents through, for example, energy efficiency, distributed generation or demand response. After a previous evaluation through PBLM of different possible strategies to reduce demand during consumption peaks, a SOM was trained to detect opportunities among users with high reduction capabilities during periods when day-ahead prices are lower than shorter-term prices. The results obtained clearly show the suitability of SOM ap-proach to find easily coherent clusters between electrical users with high demand or available response capacity, and therefore a possible way to promote customer partici-pation in electrical energy markets is opened.es
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dc.language.isoenges
dc.publisherInternational Computing Sciences Conferences Canadá (ICSC)es
dc.rightsCopyright © ICSC Academic Press Canadaes
dc.titleDevelopment of new tools to promote a more effective consumer participation in short-term electricity marketses
dc.typeinfo:eu-repo/semantics/conferenceObjectes
dc.subjectMercados de energía eléctricaes
dc.subjectPatrón de demandaes
dc.subjectSegmentación de clienteses
dc.subjectRespuesta de demandaes
dc.subjectModelado de cargaes
dc.subjectFuncionamiento del sistema de alimentaciónes
dc.subjectElectrical energy marketses
dc.subjectDemand patternes
dc.subjectCustomer segmentationes
dc.subjectDemand Responsees
dc.subjectLoad modelinges
dc.subjectPower system operationes
dc.subjectSelf organizing mapses
dc.subjectAuto organización de mapases
dc.subject.otherIngeniería Eléctricaes
dc.identifier.urihttp://hdl.handle.net/10317/1144


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