A methodology for energy prediction and optimization of a system based on the Energy Hub Concept using Particle Swarms

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/107536

dc.contributor	Universitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.contributor	Universitat Politècnica de Catalunya. MCIA - Motion Control and Industrial Applications Research Group
dc.contributor.author	Kampouropoulos, Konstantinos
dc.contributor.author	Andrade, Fabio
dc.contributor.author	Cárdenas Araújo, Juan José
dc.contributor.author	Romeral Martínez, José Luis
dc.date	2012
dc.identifier.citation	Kampouropoulos, K., Andrade, F., Cardenas, J., Romeral, L. A methodology for energy prediction and optimization of a system based on the Energy Hub Concept using Particle Swarms. A: Annual Seminar on Automation, Industrial Electronics and Instrumentation. "SAAEI 2012 The Annual Seminar on Automation, Industrial Electronics and Instrumentation, Guimaraes, Portugal". Guimaraes: 2012, p. 1-6.
dc.identifier.uri	http://hdl.handle.net/2117/107536
dc.language.iso	eng
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Enginyeria elèctrica::Producció d’energia elèctrica
dc.subject	Àrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subject	Particles
dc.subject	Force and energy
dc.subject	Forecasting
dc.subject	Energy Prediction
dc.subject	Adaptive Neuro-Fuzzy Inference System
dc.subject	Energy Hub
dc.subject	Particle Swarm Optimization
dc.subject	Partícules (Matèria)
dc.subject	Energia
dc.subject	Previsió
dc.title	A methodology for energy prediction and optimization of a system based on the Energy Hub Concept using Particle Swarms
dc.type	info:eu-repo/semantics/submittedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	In this paper, a methodology for the energy prediction for the different consumptions of a system based in the Energy Hub concept is presented. The methodology that has been used for the energy prediction is based on an Adaptive Neuro-Fuzzy Inference System. An optimization method based on Particle Swarms has been used to minimize the energy cost of a system with multiple sources such as, photovoltaic, electrical grid and natural gas.
dc.description.abstract	Peer Reviewed

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