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A Methodology for Optimization of Visual Comfort of Multi-User Intelligent Systems Based on Genetic Algorithms

  • Wendoly J. Gpe. Romero-RodríguezEmail author
  • R. Baltazar
  • Juan Martín Carpio Valadez
  • Héctor Puga
  • J. F. Mosiño
  • V. Zamudio
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 749)

Abstract

Lighting Standard in work spaces establish a range between a minimal and a maximum level of luminance depending of the task to provide visual comfort. In the state of the art, researches had been focusing to offer the adequate level of luminance just to a single user through the control of the artificial lighting systems using intelligent algorithms, without taking in account of daylight sources that can reduce energy costs. Nevertheless, an intelligent system has more than one user and a shared lighting system, there may be conflicts between users and their different activities, preferences, profiles and priorities, therefore a new approach is required. In this work, a novel methodology based on Genetic Algorithms is proposed, focusing on lights and blinds management of a multi-user scenario and it is presented. It is concentrated to find optimal configurations in energy savings, visual comfort, and conflict resolution between users based on Genetic Algorithms. Finally, the results of our proposal methodology are showed and discussed.

Keywords

Visual comfort Lighting systems Energy management Ambient intelligence GA Multi-user Conflict resolution 

References

  1. 1.
    A.K. Yener, A method of obtaining visual comfort using fixed shading devices in rooms. Build. Environ. 34(3), 285–291 (1998)CrossRefGoogle Scholar
  2. 2.
    “Reglamento Federal de Seguridad y Salud en el Trabajo,” SEGOB, D. Of. la Fed. Mèxico (2014)Google Scholar
  3. 3.
    L. Wang, Z. Wang, R. Yang, Intelligent multiagent control system for energy and comfort management in smart and sustainable buildings. IEEE Trans. Smart Grid 3(2), 605–617 (2012)CrossRefGoogle Scholar
  4. 4.
    R. Yang, Z. Wang, L. Wang, A GUI-based simulation platform for energy and comfort management in zero-energy buildings, in NAPS 2011—43rd North American Power Symposium (2011)Google Scholar
  5. 5.
    R. Yang, L. Wang, Multi-objective optimization for decision-making of energy and comfort management in building automation and control. Sustain. Cities Soc. 2(1), 1–7 (2012)CrossRefGoogle Scholar
  6. 6.
    R. Yang, L. Wang, Multi-zone building energy management using intelligent control and optimization. Sustain. Cities Soc. 6(1), 16–21 (2013)CrossRefGoogle Scholar
  7. 7.
    P.H. Shaikh, N. Bin, M. Nor, P. Nallagownden, I. Elamvazuthi, Optimized intelligent control system for indoor thermal comfort and energy management of buildings (2014)Google Scholar
  8. 8.
    T. Back, in Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms (Oxford University Press, Oxford, 1996)Google Scholar
  9. 9.
    J. Lee, Conflict resolution in multi-agent based Intelligent Environments. Build. Environ. 45(3), 574–585 (2010)CrossRefGoogle Scholar
  10. 10.
    N.O. Mexicana, “NOM-025-STPS-2008,” Condiciones iluminación en los centros Trab. (2008)Google Scholar
  11. 11.
    G. Mendez, M.A. Casillas, R. Baltazar, C. Lino, L. Mancilla, S. Lopez, Intelligent management system for the conservation of energy, in 2015 International Conference on Intelligent Environments. (2015)Google Scholar
  12. 12.
    J.A.S. Romero-Rodríguez, W.J.G. Rodríguez, V.M.Z. Flores, R.B. Sotelo-Figueroa, M.A. Alcaraz, Comparative study of BSO and GA for the optimizing energy in ambient intelligence, in Mexican International Conference on Artificial Intelligence, (Springer, Berlin, 2011), pp. 177–188Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Wendoly J. Gpe. Romero-Rodríguez
    • 1
    Email author
  • R. Baltazar
    • 1
  • Juan Martín Carpio Valadez
    • 1
  • Héctor Puga
    • 1
  • J. F. Mosiño
    • 1
  • V. Zamudio
    • 1
  1. 1.Tecnológico Nacional de MéxicoInstituto Tecnológico de LeónLeónMexico

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