Particle swarm optimization for outdoor lighting design
Autores
Castillo Martínez, Ana; Almagro Clemente, José Ramón; Gutiérrez Escolar, Alberto; Corte Valiente, Antonio del; Castillo Sequera, José Luis; [et al.]Identificadores
Enlace permanente (URI): http://hdl.handle.net/10017/43734DOI: 10.3390/en10010141
ISSN: 1996-1073
Editor
MDPI
Fecha de publicación
2017-01-23Filiación
Universidad de Alcalá. Departamento de Automática; Universidad de Alcalá. Departamento de Ciencias de la ComputaciónCita bibliográfica
Castillo Martínez, A. et al. 2017, "Particle swarm optimization for outdoor lighting design", Energies, vol. 10, no. 1, 141
Palabras clave
Energy efficiency
Lighting design
Lighting optimization
Particle Swarm Optimization (PSO)
Tipo de documento
info:eu-repo/semantics/article
Versión
info:eu-repo/semantics/publishedVersion
Versión del editor
https://doi.org/10.3390/en10010141Derechos
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Derechos de acceso
info:eu-repo/semantics/openAccess
Resumen
Outdoor lighting is an essential service for modern life. However, the high influence of this type of facility on energy consumption makes it necessary to take extra care in the design phase. Therefore, this manuscript describes an algorithm to help light designers to get, in an easy way, the best configuration parameters and to improve energy efficiency, while ensuring a minimum level of overall uniformity. To make this possible, we used a particle swarm optimization (PSO) algorithm. These algorithms are well established, and are simple and effective to solve optimization problems. To take into account the most influential parameters on lighting and energy efficiency, 500 simulations were performed using DIALux software (4.10.0.2, DIAL, Ludenscheid, Germany). Next, the relation between these parameters was studied using to data mining software. Subsequently, we conducted two experiments for setting parameters that enabled the best configuration algorithm in order to improve efficiency in the proposed process optimization.
Ficheros en el ítem
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Particle_Castillo_Energies_2017.pdf | 1.273Mb |
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Particle_Castillo_Energies_2017.pdf | 1.273Mb |
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