Please use this identifier to cite or link to this item:
http://hdl.handle.net/10553/52072
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Évora Gómez, José | en_US |
dc.contributor.author | Hernández Cabrera, José Juan | en_US |
dc.contributor.author | Hernandez, Mario | en_US |
dc.date.accessioned | 2018-11-25T17:13:55Z | - |
dc.date.available | 2018-11-25T17:13:55Z | - |
dc.date.issued | 2015 | en_US |
dc.identifier.issn | 1567-7818 | en_US |
dc.identifier.uri | http://hdl.handle.net/10553/52072 | - |
dc.description.abstract | The evaluation of the emergent behaviour in complex systems requires an analytical framework which allows the observation of different phenomena that take place at different levels. In order to observe the dynamics of complex systems, it is necessary to perform simulations so that both local and the emergent behaviour can be observed. To this end, the way in which complex system simulators are built must be examined so that it will be feasible to model large scale scenarios. In this paper, the use of Model Driven Engineering methodology is proposed to deal with this issue. Among other benefits, it is shown that this methodology allows the representation and simulation of a complex system providing support for the analysis. This analysis is supported by a metamodel which describes the system components that are under study. The application of this methodology to the development of large scale simulators is explored through a case study. This case study analyses a complex socio-technical system: a power grid. | en_US |
dc.language | eng | en_US |
dc.relation | Framework Para la Simulación de la Gestión de Mercado y Técnica de Redes Eléctricas Insulares Basado en Agentes Inteligentes. Caso de la Red Eléctrica de Gran Canaria. | en_US |
dc.relation.ispartof | Natural Computing | en_US |
dc.source | Natural Computing [ISSN 1567-7818], v. 14 (1), p. 129-144 | en_US |
dc.subject | 120304 Inteligencia artificial | en_US |
dc.subject.other | Agent based model | en_US |
dc.subject.other | Electrical system | en_US |
dc.subject.other | Domestic load curve simulation | en_US |
dc.subject.other | Residential demand | en_US |
dc.subject.other | Demand side management | en_US |
dc.subject.other | Appliances | en_US |
dc.subject.other | Model driven engineering | en_US |
dc.subject.other | Simulation framework | en_US |
dc.subject.other | Demand size management | en_US |
dc.subject.other | Complex system | en_US |
dc.title | Advantages of model driven engineering for studying complex systems | en_US |
dc.type | info:eu-repo/semantics/Article | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1007/s11047-014-9469-y | en_US |
dc.identifier.scopus | 84925484060 | - |
dc.identifier.isi | 000360155600010 | - |
dc.contributor.authorscopusid | 55765472900 | - |
dc.contributor.authorscopusid | 56109424300 | - |
dc.contributor.authorscopusid | 7401972145 | - |
dc.contributor.authorscopusid | 57212239402 | - |
dc.identifier.eissn | 1572-9796 | - |
dc.description.lastpage | 144 | en_US |
dc.identifier.issue | 1 | - |
dc.description.firstpage | 129 | en_US |
dc.relation.volume | 14 | en_US |
dc.investigacion | Ingeniería y Arquitectura | en_US |
dc.type2 | Artículo | en_US |
dc.contributor.daisngid | 4682726 | - |
dc.contributor.daisngid | 3554947 | - |
dc.contributor.daisngid | 21915345 | - |
dc.utils.revision | Sí | en_US |
dc.contributor.wosstandard | WOS:Evora, J | - |
dc.contributor.wosstandard | WOS:Hernandez, JJ | - |
dc.contributor.wosstandard | WOS:Hernandez, M | - |
dc.date.coverdate | Enero 2015 | en_US |
dc.identifier.ulpgc | Sí | en_US |
dc.description.sjr | 0,417 | |
dc.description.jcr | 1,31 | |
dc.description.sjrq | Q2 | |
dc.description.jcrq | Q2 | |
dc.description.scie | SCIE | |
item.grantfulltext | none | - |
item.fulltext | Sin texto completo | - |
crisitem.project.principalinvestigator | Hernández Tejera, Francisco Mario | - |
crisitem.author.dept | GIR SIANI: Inteligencia Artificial, Redes Neuronales, Aprendizaje Automático e Ingeniería de Datos | - |
crisitem.author.dept | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
crisitem.author.dept | Departamento de Informática y Sistemas | - |
crisitem.author.dept | GIR SIANI: Inteligencia Artificial, Redes Neuronales, Aprendizaje Automático e Ingeniería de Datos | - |
crisitem.author.dept | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
crisitem.author.dept | Departamento de Informática y Sistemas | - |
crisitem.author.dept | GIR SIANI: Inteligencia Artificial, Redes Neuronales, Aprendizaje Automático e Ingeniería de Datos | - |
crisitem.author.dept | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
crisitem.author.dept | Departamento de Informática y Sistemas | - |
crisitem.author.orcid | 0000-0003-2427-2441 | - |
crisitem.author.orcid | 0000-0001-9717-8048 | - |
crisitem.author.parentorg | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
crisitem.author.parentorg | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
crisitem.author.parentorg | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
crisitem.author.fullName | Évora Gómez, José | - |
crisitem.author.fullName | Hernández Cabrera, José Juan | - |
crisitem.author.fullName | Hernández Tejera, Francisco Mario | - |
Appears in Collections: | Artículos |
SCOPUSTM
Citations
5
checked on Nov 17, 2024
WEB OF SCIENCETM
Citations
5
checked on Nov 17, 2024
Page view(s)
138
checked on Sep 28, 2024
Google ScholarTM
Check
Altmetric
Share
Export metadata
Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.