Advertisement

Intelligent Lighting Control System

  • Elena GarcíaEmail author
  • Sara Rodríguez
  • Juan F. De Paz
  • Javier Bajo
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 290)

Abstract

This paper presents an adaptive architecture that allows centralized control of public lighting and intelligent management, in order to economise on lighting and maintain maximum comfort status of the illuminated areas. To carry out this management, architecture merges various techniques of artificial intelligence (AI) and statistics such as artificial neural networks (ANN), multi-agent systems (MAS), EM algorithm, methods based on ANOVA and a Service Oriented Aproach (SOA). It performs optimization both energy consumption and economically from a modular architecture and fully adaptable to the current lighting systems possible. The architecture has been tested and validated successfully and continues its development today.

Keywords

Light sensors intelligent systems distributed systems Autonomous control Street lighting 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    IDAE, Instituto para la Diversificacion y Ahorro de la Energía, http://www.idae.es/ (last access January 12, 2014)
  2. 2.
    Gómez-Romero, J., Serrano, M.A., Patricio, M.A., García, J., Molina, J.M.: Context-based Scene Recognition from Visual Data in Smart Homes: An Information Fusion approach. ACM/Springer Journal of Personal and Ubiquitous Computing. Special Issue on Sensor-driven Computing and Applications for Ambient Intelligence 16(7), 835–857 (2012)Google Scholar
  3. 3.
    Chourabi, H., Nam, T., Walker, S., Gil-Garcia, J.R., Mellouli, S., Nahon, K., Pardo, T.A., Scholl, H.J.: Understanding Smart Cities: An Integrative Framework. In: 45th Hawaii International Conference on System Sciences, pp. 2289–2297 (2012)Google Scholar
  4. 4.
    Washburn, D., Sindhu, U., Balaouras, S., Dines, R.A., Hayes, N., Nelson, L.E.: Helping CIOs Understand “Smart City” Initiatives. Growth 17 (2009), http://c3328005.r5.cf0.rackcdn.com/73efa931-0fac-4e28-ae77-8e58ebf74aa6.pdf
  5. 5.
    Daigneau, R.: Service Design Patterns: Fundamental Design Solutions for SOAP/WSDL and RESTful Web Services, 1st edn. Addison-Wesley Professional (2013)Google Scholar
  6. 6.
    Afshari, S., Mishra, S., Julius, A., Lizarralde, F., Wason, J.D., Wen, J.T.: Modeling and control of color tunable lighting systems. Energy and Buildings 68(Pt. A), 242–253 (2014)CrossRefGoogle Scholar
  7. 7.
    Wojnicki, I., Ernst, S., Kotulski, L., Se¸dziwy, A.: Advanced street lighting control. Expert Systems with Applications 41(4 Pt. 1), 999–1005 (2014)CrossRefGoogle Scholar
  8. 8.
    Carrillo, C., Diaz-Dorado, E., Cidrás, J., Bouza-Pregal, A., Falcón, P., Fernández, A., Álvarez-Sánchez, A.: Lighting control system based on digital camera for energy saving in shop windows. Energy and Buildings 59, 143–151 (2013)CrossRefGoogle Scholar
  9. 9.
    Yang, C., Fan, S., Wang, Z., Li, W.: Application of fuzzy control method in a tunnel lighting system. Mathematical and Computer Modelling 54(3-4), 931–937 (2011)CrossRefzbMATHGoogle Scholar
  10. 10.
    Zhang, J., Qiao, G., Song, G., Sun, H., Ge, J.: Group decision making based autonomous control system for street lighting. Measurement 46(1), 108–116 (2013)CrossRefGoogle Scholar
  11. 11.
    Bourgeois, D., Reinhart, C., Macdonald, I.: Adding advanced behavioural models in whole building energy simulation: A study on the total energy impact of manual and automated lighting control. Energy and Buildings 38(7), 814–823 (2006)CrossRefGoogle Scholar
  12. 12.
    Barra, K., Rahem, D.: Predictive direct power control for photovoltaic grid connected system: An approach based on multilevel converters. Energy Conversion and Management 78, 825–834 (2014)CrossRefGoogle Scholar
  13. 13.
    Miller Jr., R.G.: Beyond ANOVA: basics of applied statistics. CRC Press (1997)Google Scholar
  14. 14.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data Via the EM Algorithm. Journal of the Royal Statistical 39, 1–38 (1977)zbMATHMathSciNetGoogle Scholar
  15. 15.
    Little, R., Rubin, D.: Statistical Analysis with Missing Data. Wiley & Son, New York (2002)zbMATHGoogle Scholar
  16. 16.
    Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall (1998) ISBN 0-13-273350-1Google Scholar
  17. 17.
    Collobert, R., Bengio, S.: Links between Perceptrons, MLPs and SVMs. In: Proc. Int’l Conf. on Machine Learning, ICML (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Elena García
    • 1
    Email author
  • Sara Rodríguez
    • 1
  • Juan F. De Paz
    • 1
  • Javier Bajo
    • 2
  1. 1.Computer and Automation DepartmentUniversity of SalamancaSalamancaSpain
  2. 2.Artificial Intelligence DepartmentPolytechnic University of MadridMadridSpain

Personalised recommendations