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Particle Swarm Optimization for Outdoor Lighting Design

Author

Listed:
  • Ana Castillo-Martinez

    (Department of Computer Sciences, Polytechnic School, University of Alcala, Madrid-Barcelona Road, Km 33.6, 28871 Alcala de Henares, Spain)

  • Jose Ramon Almagro

    (Airbus Defence and Space, Gunnels Wood Road, Stevenage, Hertfordshire SG12AS, UK)

  • Alberto Gutierrez-Escolar

    (Department of Computer Sciences, Polytechnic School, University of Alcala, Madrid-Barcelona Road, Km 33.6, 28871 Alcala de Henares, Spain)

  • Antonio Del Corte

    (Department of Computer Engineering, Polytechnic School, University of Alcala, Madrid-Barcelona Road, Km 33.6, Alcala de Henares 28871, Spain)

  • José Luis Castillo-Sequera

    (Department of Computer Sciences, Polytechnic School, University of Alcala, Madrid-Barcelona Road, Km 33.6, 28871 Alcala de Henares, Spain)

  • José Manuel Gómez-Pulido

    (Department of Computer Sciences, Polytechnic School, University of Alcala, Madrid-Barcelona Road, Km 33.6, 28871 Alcala de Henares, Spain)

  • José-María Gutiérrez-Martínez

    (Department of Computer Sciences, Polytechnic School, University of Alcala, Madrid-Barcelona Road, Km 33.6, 28871 Alcala de Henares, Spain)

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.

Suggested Citation

  • Ana Castillo-Martinez & Jose Ramon Almagro & Alberto Gutierrez-Escolar & Antonio Del Corte & José Luis Castillo-Sequera & José Manuel Gómez-Pulido & José-María Gutiérrez-Martínez, 2017. "Particle Swarm Optimization for Outdoor Lighting Design," Energies, MDPI, vol. 10(1), pages 1-11, January.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:1:p:141-:d:88578
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    References listed on IDEAS

    as
    1. Kostic, Miomir & Djokic, Lidija, 2009. "Recommendations for energy efficient and visually acceptable street lighting," Energy, Elsevier, vol. 34(10), pages 1565-1572.
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    Cited by:

    1. Alberto Garces-Jimenez & Jose-Manuel Gomez-Pulido & Nuria Gallego-Salvador & Alvaro-Jose Garcia-Tejedor, 2021. "Genetic and Swarm Algorithms for Optimizing the Control of Building HVAC Systems Using Real Data: A Comparative Study," Mathematics, MDPI, vol. 9(18), pages 1-24, September.
    2. Ana Ogando-Martínez & Javier López-Gómez & Lara Febrero-Garrido, 2018. "Maintenance Factor Identification in Outdoor Lighting Installations Using Simulation and Optimization Techniques," Energies, MDPI, vol. 11(8), pages 1-13, August.

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