Applied Intelligence

, Volume 18, Issue 2, pp 155–177 | Cite as

Fuzzy Control of HVAC Systems Optimized by Genetic Algorithms

  • Rafael Alcalá
  • Jose M. Benítez
  • Jorge Casillas
  • Oscar Cordón
  • Raúl Pérez

Abstract

This paper presents the use of genetic algorithms to develop smartly tuned fuzzy logic controllers dedicated to the control of heating, ventilating and air conditioning systems concerning energy performance and indoor comfort requirements. This problem has some specific restrictions that make it very particular and complex because of the large time requirements existing due to the need of considering multiple criteria (which enlarges the solution search space) and to the long computation time models require to assess the accuracy of each individual.

To solve these restrictions, a genetic tuning strategy considering an efficient multicriteria approach has been proposed. Several fuzzy logic controllers have been produced and tested in laboratory experiments in order to check the adequacy of such control and tuning technique. To do so, accurate models of the controlled buildings (two real test sites) have been provided by experts. Finally, simulations and real experiments were compared determining the effectiveness of the proposed strategy.

HVAC systems fuzzy logic controllers genetic tuning multiple criteria 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    M. Bruant, G. Guarracino, P. Michel, A. Voeltzel, and M. Santamouris, “Impact of a global control of bioclimatic buildings in terms of energy consumption and building's performance,” in Proc. of the 4th European Conference on Solar Architecture and Urban Planning, Berlin, pp. 537–540, 1996.Google Scholar
  2. 2.
    D. Driankov, H. Hellendoorn, and M. Reinfrank, An Introduction to Fuzzy Control, Springer-Verlag, 1993.Google Scholar
  3. 3.
    E.H. Mamdani, “Applications of fuzzy algorithms for control a simple dynamic plant,” in Proc. of the IEEE, vol. 121, no.12, pp. 1585–1588, 1974.Google Scholar
  4. 4.
    E.H. Mamdani and S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller,” International Journal of Man-Machine Studies, vol. 7, pp. 1–13, 1975.Google Scholar
  5. 5.
    R. Alcalá, J. Casillas, J.L. Castro, A. González, and F. Herrera, “A multicriteria genetic tuning for fuzzy logic controllers,” Mathware and Soft Computing, vol. 8, no.2, pp. 179–201, 2001.Google Scholar
  6. 6.
    D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989.Google Scholar
  7. 7.
    Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, 1996.Google Scholar
  8. 8.
    O. Cordón, F. Herrera, F. Hoffmann, and L. Magdalena, Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases, World Scientific: Singapore, 2001.Google Scholar
  9. 9.
    M. Arima, E.H. Hara, and J.D. Katzberg, “A fuzzy logic and rough sets controller for HVAC systems,” in Proc. of the IEEE WESCANEX’95, vol. 1, NY, 1995, pp. 133–138.Google Scholar
  10. 10.
    P.Y. Glorennec, “Application of fuzzy control for building energy management,” in Building Simulation: International Building Performance Simulation Association 1, Sophia Antipolis: France, 1991, pp. 197–201.Google Scholar
  11. 11.
    S. Huang, and R.M. Nelson, “Rule development and adjustment strategies of a fuzzy logic controller for an HVAC system—Parts I and II (analysis and experiment),” ASHRAE Transactions, vol. 100, no.1, pp. 841–850, 851–856, 1994.Google Scholar
  12. 12.
    L.A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, pp. 338–353, 1965.Google Scholar
  13. 13.
    M. Sugeno and G.T. Kang, “Structure identification of fuzzy model,” Fuzzy Sets and Systems, vol. 28, pp. 15–33, 1988.Google Scholar
  14. 14.
    T. Takagi and M. Sugeno, “Fuzzy identification of systems and its application to modeling and control,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 15, no.1, pp. 116–132, 1985.Google Scholar
  15. 15.
    A. Bardossy and L. Duckstein, Fuzzy Rule-Based Modeling with Application to Geophysical, Biological and Engineering Systems, CRC Press, 1995.Google Scholar
  16. 16.
    O. Cordón, F. Herrera, and A. Peregrín, “Applicability of the fuzzy operators in the design of fuzzy logic controllers,” Fuzzy Sets and Systems, vol. 86, pp. 15–41, 1997.Google Scholar
  17. 17.
    L.X. Wang, Adaptive Fuzzy Systems and Control. Design and Stability Analysis, Prentice-Hall, 1994.Google Scholar
  18. 18.
    P.P. Bonissone, “Fuzzy logic controllers: An introduction reality,” in Computational Intelligence: Imitating Life, edited by J.M. Zurada, R.J. Marks II, and C.J. Robinson, IEEE Press, 1994, pp. 316–327.Google Scholar
  19. 19.
    C.C. Lee, “Fuzzy logic in control systems: Fuzzy logic controller—Parts I and II,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 20, pp. 404–418, 419–435, 1990.Google Scholar
  20. 20.
    R. Palm, D. Driankov, and H. Hellendoorn, Model Based Fuzzy Control, Springer-Verlag, 1997.Google Scholar
  21. 21.
    L.A. Sánchez and J.A. Corrales, “Niching scheme for steady state GA-P and its application to fuzzy rule based classifiers induction,” Mathware and Soft Computing, vol. 7, nos.2/3, pp. 337–350, 2000.Google Scholar
  22. 22.
    J. Kiszka, M. Kochanska, and D. Sliwinska, “The influence of some fuzzy implication operators on the accuracy of a fuzzy model—Parts I and II,” Fuzzy Sets and Systems, vol. 15, pp. 111–128, 223–240, 1985.Google Scholar
  23. 23.
    A.E. Gegov and P.M. Frank, “Hierarchical fuzzy control of multivariable systems,” Fuzzy Sets and Systems, vol. 72, pp. 299–310, 1995.Google Scholar
  24. 24.
    R.R. Yager, “On the construction of hierarchical fuzzy systems model,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 22, pp. 1414–1427, 1992.Google Scholar
  25. 25.
    M. Delgado, M.A. Vila, and W. Voxman, “On a canonical representation of fuzzy numbers,” Fuzzy Sets and Systems, vol. 93, no.1, pp. 125–135, 1998.Google Scholar
  26. 26.
    O. Cord´on and F. Herrera, “A three-stage evolutionary process for learning descriptive and approximative fuzzy logic controller knowledge bases from examples,” International Journal of Approximate Reasoning, vol. 17, no.4, pp. 369–407, 1997.Google Scholar
  27. 27.
    F. Herrera, M. Lozano, and J.L. Verdegay, “Tuning fuzzy controllers by genetic algorithms,” International Journal of Approximate Reasoning, vol. 12, pp. 299–315, 1995.Google Scholar
  28. 28.
    C. Karr, “Genetic algorithms for fuzzy controllers,” AI Expert, pp. 26–33, 1991.Google Scholar
  29. 29.
    C.M. Fonseca and P.J. Fleming, “An overview of evolutionary algorithms in multiobjective optimization,” Evolutionary Computation, vol. 3, pp. 1–16, 1995.Google Scholar
  30. 30.
    D. Whitley and J. Kauth, “GENITOR: A different genetic algorithm,” in Proc. of the Rocky Mountain Conference on Artificial Intelligence, Denver, 1988, pp. 118–130,.Google Scholar
  31. 31.
    F. Herrera, M. Lozano, and J.L. Verdegay, “Tackling real-coded genetic algorithms: Operators and tools for the behaviour analysis,” Artificial Intelligence Review, vol. 12, pp. 265–319, 1998.Google Scholar
  32. 32.
    J.H. Holland, Adaptation in Natural and Artificial Systems, The University of Michigan Press: Ann Arbor, 1975 (The MIT Press, London, 1992).Google Scholar
  33. 33.
    F. Herrera, M. Lozano, and J.L. Verdegay, “Fuzzy connectives based crossover operators to model genetic algorithms population diversity,” Fuzzy Sets and Systems, vol. 92, no.1, pp. 21–30, 1997.Google Scholar
  34. 34.
    J.E. Baker, “Reducing bias and inefficiency in the selection algorithm,” in Proc. of the 2nd International Conference on Genetic Algorithms, edited by J.J. Grefenstette, Lawrence Erlbaum: Hillsdale, NJ, 1987, pp. 14–21.Google Scholar
  35. 35.
    L.J. Eshelman, The CHC Adaptive Search Algorithm: How to Have Safe Search when Engaging in Nontraditional Genetic Recombination, Morgan Kauffman: San Mateo, CA, 1990.Google Scholar

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Rafael Alcalá
    • 1
  • Jose M. Benítez
    • 2
  • Jorge Casillas
    • 2
  • Oscar Cordón
    • 2
  • Raúl Pérez
    • 2
  1. 1.Department of Computer ScienceUniversity of JaénJaénSpain
  2. 2.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain

Personalised recommendations