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Maintenance Factor Identification in Outdoor Lighting Installations Using Simulation and Optimization Techniques

Author

Listed:
  • Ana Ogando-Martínez

    (School of Industrial Engineering, University of Vigo, Campus Universitario, 36310 Vigo, Spain)

  • Javier López-Gómez

    (School of Industrial Engineering, University of Vigo, Campus Universitario, 36310 Vigo, Spain)

  • Lara Febrero-Garrido

    (Defense University Center, Spanish Naval Academy, Plaza de España, s/n, 36920 Marín, Spain)

Abstract

This document addresses the development of a novel methodology to identify the actual maintenance factor of the luminaires of an outdoor lighting installation in order to assess their lighting performance. The method is based on the combined use of Radiance, a free and open-source tool, for the modeling and simulation of lighting scenes, and GenOpt, a generic optimization program, for the calibration of the model. The application of this methodology allows the quantification of the deterioration of the road lighting system and the identification of luminaires that show irregularities in their operation. Values lower than 9% for the error confirm that this research can contribute to the management of street lighting by assessing real road conditions.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:2169-:d:164593
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    References listed on IDEAS

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    1. Germán Ramos Ruiz & Carlos Fernández Bandera, 2017. "Validation of Calibrated Energy Models: Common Errors," Energies, MDPI, vol. 10(10), pages 1-19, October.
    2. Baloch, Ashfaque Ahmed & Shaikh, Pervez Hameed & Shaikh, Faheemullah & Leghari, Zohaib Hussain & Mirjat, Nayyar Hussain & Uqaili, Muhammad Aslam, 2018. "Simulation tools application for artificial lighting in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3007-3026.
    3. 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.
    4. Steffen Thielemans & Dario Di Zenobio & Abdellah Touhafi & Philippe Lataire & Kris Steenhaut, 2017. "DC Grids for Smart LED-Based Lighting: The EDISON Solution," Energies, MDPI, vol. 10(10), pages 1-26, September.
    5. Antonio Del Corte-Valiente & José Luis Castillo-Sequera & Ana Castillo-Martinez & José Manuel Gómez-Pulido & Jose-Maria Gutierrez-Martinez, 2017. "An Artificial Neural Network for Analyzing Overall Uniformity in Outdoor Lighting Systems," Energies, MDPI, vol. 10(2), pages 1-18, February.
    6. Alfonso Gago-Calderón & Manuel Jesús Hermoso-Orzáez & Jose Ramon De Andres-Diaz & Guillermo Redrado-Salvatierra, 2018. "Evaluation of Uniformity and Glare Improvement with Low Energy Efficiency Losses in Street Lighting LED Luminaires Using Laser-Sintered Polyamide-Based Diffuse Covers," Energies, MDPI, vol. 11(4), pages 1-17, April.
    7. Bustamante, Waldo & Uribe, Daniel & Vera, Sergio & Molina, Germán, 2017. "An integrated thermal and lighting simulation tool to support the design process of complex fenestration systems for office buildings," Applied Energy, Elsevier, vol. 198(C), pages 36-48.
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    Cited by:

    1. Francisco G. Montoya & Raúl Baños & Alfredo Alcayde & Francisco Manzano-Agugliaro, 2019. "Optimization Methods Applied to Power Systems," Energies, MDPI, vol. 12(12), pages 1-8, June.

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