Particle swarm optimization for outdoor lighting design

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 c...

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Detalles Bibliográficos
Autores: Castillo Martínez, Ana|||0000-0001-9445-5871, Almagro Clemente, José Ramón, Gutiérrez Escolar, Alberto, Corte Valiente, Antonio del|||0000-0001-7334-2317, Castillo Sequera, José Luis|||0000-0002-9131-1618, Gómez Pulido, José Manuel|||0000-0002-6897-8262, Gutiérrez Martínez, José María|||0000-0003-0134-1256
Tipo de recurso: artículo
Fecha de publicación:2017
País:España
Institución:Universidad de Alcalá (UAH)
Repositorio:e_Buah Biblioteca Digital Universidad de Alcalá
Idioma:inglés
OAI Identifier:oai:ebuah.uah.es:10017/43734
Acceso en línea:http://hdl.handle.net/10017/43734
https://dx.doi.org/10.3390/en10010141
Access Level:acceso abierto
Palabra clave:Energy efficiency
Lighting design
Lighting optimization
Particle Swarm Optimization (PSO)
Informática
Computer science
Descripción
Sumario: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.