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dc.contributor.authorCárdenas Araújo, Juan José
dc.contributor.authorGarcía Espinosa, Antonio
dc.contributor.authorRomeral Martínez, José Luis
dc.contributor.authorKampouropoulos, Konstantinos
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Elèctrica
dc.date.accessioned2012-07-17T13:17:47Z
dc.date.created2011
dc.date.issued2011
dc.identifier.citationCardenas, J. [et al.]. Evolutive ANFIS training for energy load profile forecast for an IEMS in an automated factory. A: IEEE International Conference on Emerging Technologies and Factory Automation. "Emerging Technologies & Factory Automation (ETFA), 2011 IEEE 16th Conference on". Toulouse: 2011, p. 1-8.
dc.identifier.isbn1946-0740
dc.identifier.urihttp://hdl.handle.net/2117/16270
dc.description.abstractIn this paper an evolutive algorithm is used to train an adaptative-network-based fuzzy inference system (ANFIS), particularly a genetic algorithm (GA). The GA is able to train the antecedent and consequent parameters of an ANFIS, which is used for energy load profile forecasting in an automated factory. This load forecasting is useful to support an intelligent energy management system (IEMS), which enables the user to optimize the energy consumptions by means of getting the optimal work points, scheduling the production according to these points, etc. The proposed training algorithm showed excellent results with complex plants like industrial energy consumers in the user-side, where the randomness of the loads is higher than in utility loads. Real data from an automated car factory were used to test the presented algorithms. Appropriated results were obtained.
dc.format.extent8 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Energies::Energia elèctrica::Automatització i control de l'energia elèctrica
dc.subject.lcshGenetic algorithms
dc.subject.lcshAdaptative-network-based fuzzy inference system
dc.titleEvolutive ANFIS training for energy load profile forecast for an IEMS in an automated factory
dc.typeConference report
dc.subject.lemacAlgorismes genètics
dc.subject.lemacANFIS
dc.contributor.groupUniversitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group
dc.identifier.doi10.1109/ETFA.2011.6059079
dc.description.peerreviewedPeer Reviewed
dc.rights.accessRestricted access - publisher's policy
drac.iddocument9466316
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
upcommons.citation.authorCardenas, J.; Garcia, A.; Romeral, L.; Kampouropoulos, K.
upcommons.citation.contributorIEEE International Conference on Emerging Technologies and Factory Automation
upcommons.citation.pubplaceToulouse
upcommons.citation.publishedtrue
upcommons.citation.publicationNameEmerging Technologies & Factory Automation (ETFA), 2011 IEEE 16th Conference on
upcommons.citation.startingPage1
upcommons.citation.endingPage8


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