Advertisement

Low Quality Data Management for Optimising Energy Efficiency in Distributed Agents

  • Jose R. Villar
  • Enrique de la Cal
  • Javier Sedano
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 71)

Abstract

Energy efficiency represents one of the main challenges in the engineering field. The benefit of the energy efficiency is twofold: the reduction of the cost owing to the energy consumption and the reduction in the energy consumption due to a better design minimising the energy losses. This is particularly true in real world processes in the industry or in business, where the elements involved may be considered as distributed agents. Moreover, in some fields like building management systems the data are full of noise and biases, and the emergence of new technologies -as the ambient intelligence can be- 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 in intelligent distributed systems. We will introduce the problem and some of the challenging fields are to be detailed. Finally, a brief review of methods considering the low quality data is related.

Keywords

Genetic Fuzzy Systems Low quality data Energy Efficiency Building Automation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Alcalá, R., Alcalá-Fdez, J., Gacto, M.J.: Improving fuzzy logic controllers obtained by experts: a case study in HVAC systems. Applied Intelligence 31, 15–30 (2009)CrossRefGoogle Scholar
  2. 2.
    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
  3. 3.
    Booy, D., Liu, K., Qiao, B., Guy, C.: A semiotic multi-agent system for intelligent building control. In: Ambi-Sys 2008: Proceedings of the 1st international conference on Ambient media and systems (2008)Google Scholar
  4. 4.
    Couso, I., Sánchez, L.: Higher order models for fuzzy random variables. Fuzzy Sets and Systems 159, 237–258 (2008)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Davidsson, P., Boman, M.: Distributed monitoring and control of office buildings by embedded agents. Information Sciences 171, 293–307 (2005)CrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    Díaz, J., Rodríguez, E., Hurtado, L., Cacique, H., Ramírez, A., Vázquez, N.: LightNet a Reliable Option for Lighting Applications. In: Proceedings of the International Conference on Advances in Electronics and Micro-electronics, pp. 156–164. IEEE Computer Society, Los Alamitos (2008)Google Scholar
  8. 8.
    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
  9. 9.
    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
  10. 10.
    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
  11. 11.
    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
  12. 12.
    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
  13. 13.
    Kim, S.Y., Kim, J.J.: Influence of light fluctuation on occupant visual perception. Building and Environment 42(8), 2888–2899 (2007)CrossRefGoogle Scholar
  14. 14.
    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
  15. 15.
    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
  16. 16.
    Mady, A., Boubekeur, M., Provan, G.: Compositional Model-Driven Design of Embedded Code for Energy-Efficient Buildings. In: Proceedings of the 2009 IEEE International Conference on Industrial Informatics, pp. 250–255 (2009)Google Scholar
  17. 17.
    Malastras, A., Asgari, A.H., Baugé, T.: Web Enabled Wireless Sensor Networks for Facilities Management. IEEE Systems Journal 2(4), 500–512 (2008)CrossRefGoogle Scholar
  18. 18.
    Martín, J.A., Gil, A.J.: A new heuristic approach for distribution systems loss reduction. Electric Power Systems Research 78(11), 1953–1958 (2008)CrossRefGoogle Scholar
  19. 19.
    Park, T.J., Hong, S.H.: Development of an Experimental Model of BACnet-based Lighting Control System. In: Proceedings of the 2006 IEEE International Conference on Industrial Informatics, pp. 114–119 (2006)Google Scholar
  20. 20.
    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
  21. 21.
    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
  22. 22.
    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
  23. 23.
    Sánchez, L., Suárez, M.R., Villar, J.R., Couso, I.: Mutual Information-based Feature Selection and Fuzzy Discretization of Vague Data. Internacional Journal of Approximate Reasoning 49, 607–622 (2008)CrossRefGoogle Scholar
  24. 24.
    Sánchez, L., Couso, I., Casillas, J.: Genetic Learning of Fuzzy Rules based on Low Quality Data. Fuzzy Sets and Systems (2009)Google Scholar
  25. 25.
    Van Hulse, J., Khoshgoftaar, T.M.: A comprehensive empirical evaluation of missing value imputation in noisy software measurement data. The Journal of Systems and Software 81, 691–708 (2008)Google Scholar
  26. 26.
    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. Springer, Heidelberg (2009)Google Scholar
  27. 27.
    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
  28. 28.
    Villar, J.R., Otero, A., Otero, J., Sánchez, L.: Taximeter verification with GPS and Soft Computing Techniques. Soft Computing 14(4), 405–418 (2010)CrossRefGoogle Scholar
  29. 29.
    Zhu, Y., Tomsovic, K.: Optimal distribution power flow for systems with distributed energy resources. International Journal of Electrical Power & Energy Systems 29(3), 260–267 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Jose R. Villar
    • 1
  • Enrique de la Cal
    • 1
  • Javier Sedano
    • 2
  1. 1.University of OviedoGijónSpain
  2. 2.Instituto Tecnológico de Castilla y LeónBurosSpain

Personalised recommendations