Evaluating the Low Quality Measurements in Lighting Control Systems

  • Jose R. Villar
  • Enrique de la Cal
  • Javier Sedano
  • Marco García
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 73)


In real world processes in the industry or in business, where the elements involved generate data full of noise and biases, improving the energy efficiency represents one of the main challenges. In other fields as lighting control systems, the emergence of new technologies, such as the Ambient Intelligence, also degrades the quality data introducing linguistic values. In this contribution we propose the use of the novel genetic fuzzy system approach to obtain classifiers and models able to manage low quality data to improve the energy efficiency. The problem is introduced through the experimentation to figure out how significant the improvement of managing the low quality data can be.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bernal-Agustín, J.L., Dufo-López, R.: Techno-economical optimization of the production of hydrogen from PV-Wind systems connected to the electrical grid. Renewable Energy 35(4), 747–758 (2010)CrossRefGoogle Scholar
  2. 2.
    Couso, I., Sánchez, L.: Higher order models for fuzzy random variables. Fuzzy Sets and Systems 159, 237–258 (2008)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    de Keyser, R., Ionescu, C.: Modelling and simulation of a lighting control system. Simulation Modelling Practice and Theory (2009), doi: 10.1016/j.simpat.2009.10.003Google Scholar
  4. 4.
    Doulos, L., Tsangrassoulis, A., Topalis, F.V.: The role of spectral response of photosensors in daylight responsive systems. Energy and Buildings 40(4), 588–599 (2008)CrossRefGoogle Scholar
  5. 5.
    Folleco, A.A., Khoshgoftaar, T.M., Van Hulse, J., Napolitano, A.: Identifying Learners Robust to Low Quality Data. Informatica 33, 245–259 (2009)zbMATHGoogle Scholar
  6. 6.
    Gligor, A., Grif, H., Oltean, S.: Considerations on an Intelligent Buildings Management System for an Optimized Energy Consumption. In: Proceedings of the IEEE Conference on Automation, Quality and Testing, Robotics (2006)Google Scholar
  7. 7.
    Hviid, C.A., Nielsen, T.R., Svendsen, S.: Simple tool to evaluate the impact of daylight on building energy consumption. Solar Energy (2009), doi:10.1016/j.solener.2008.03.001Google Scholar
  8. 8.
    Houwing, M., Ajah, A.N., Heijnen, P.W., Bouwmans, I., Herder, P.M.: Uncertainties in the design and operation of distributed energy resources: The case of micro-CHP systems. Energy 33(10), 1518–1536 (2008)CrossRefGoogle Scholar
  9. 9.
    Li, D.H.W., Cheung, K.L., Wong, S.L., Lam, T.N.T.: An analysis of energy-efficient light fittings and lighting controls. Applied Energy 87(2), 558–567 (2010)CrossRefGoogle Scholar
  10. 10.
    Luengo, J., Herrera, F.: Domains of competence of fuzzy rule based classification systems with data complexity measures: A case of study using a fuzzy hybrid genetic based machine learning method. Fuzzy Sets and Systems 161, 3–19 (2010)CrossRefGoogle Scholar
  11. 11.
    Qiao, B., Liu, K., Guy, C.: A Multi-Agent System for Building Control. In: IAT 2006: Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology, pp. 653–659. IEEE Computer Society, Los Alamitos (2006)CrossRefGoogle Scholar
  12. 12.
    Sánchez, L., Couso, I.: Advocating the Use of Imprecisely Observed Data in Genetic Fuzzy Systems. IEEE Transactions on Fuzzy Systems 15(4), 551–562 (2007)CrossRefGoogle Scholar
  13. 13.
    Sánchez, L., Otero, J.: Learning Fuzzy Linguistic Models from Low Quality Data by Genetic Algorithms. In: Proceedings of the IEEE Internacional Conference on Fuzzy Systems FUZZ-IEEE 2007 (2007)Google Scholar
  14. 14.
    Sánchez, L., Couso, I., Casillas, J.: Genetic Learning of Fuzzy Rules based on Low Quality Data. Fuzzy Sets and Systems (2009)Google Scholar
  15. 15.
    Villar, J.R., Pérez, R., de la Cal, E., Sedano, J.: Efficiency in Electrical Heating Systems: An MAS real World Application. In: Proceedings of the 7th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2009). LNCS, vol. 55, pp. 460–469 (2009)Google Scholar
  16. 16.
    Villar, J.R., de la Cal, E., Sedano, J.: A fuzzy logic based efficient energy saving approach for domestic heating systems. Integrated Computer-Aided Engineering 16(2), 151–164 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jose R. Villar
    • 1
  • Enrique de la Cal
    • 1
  • Javier Sedano
    • 2
  • Marco García
    • 1
  1. 1.University of OviedoGijónSpain
  2. 2.University of OviedoBurgosSpain

Personalised recommendations