Particle swarm optimization for outdoor lighting design
Authors
Castillo Martínez, AnaIdentifiers
Permanent link (URI): http://hdl.handle.net/10017/43734DOI: 10.3390/en10010141
ISSN: 1996-1073
Publisher
MDPI
Date
2017-01-23Affiliation
Universidad de Alcalá. Departamento de Automática; Universidad de Alcalá. Departamento de Ciencias de la ComputaciónBibliographic citation
Castillo Martínez, A. et al. 2017, "Particle swarm optimization for outdoor lighting design", Energies, vol. 10, no. 1, 141
Keywords
Energy efficiency
Lighting design
Lighting optimization
Particle Swarm Optimization (PSO)
Document type
info:eu-repo/semantics/article
Version
info:eu-repo/semantics/publishedVersion
Publisher's version
https://doi.org/10.3390/en10010141Rights
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Access rights
info:eu-repo/semantics/openAccess
Abstract
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.
Files in this item
Files | Size | Format |
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Particle_Castillo_Energies_2017.pdf | 1.273Mb |
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Files | Size | Format |
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Particle_Castillo_Energies_2017.pdf | 1.273Mb |
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