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.
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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.
D. Driankov, H. Hellendoorn, and M. Reinfrank, An Introduction to Fuzzy Control, Springer-Verlag, 1993.
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.
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.
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.
D.E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989.
Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, 1996.
O. Cordón, F. Herrera, F. Hoffmann, and L. Magdalena, Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases, World Scientific: Singapore, 2001.
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.
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.
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.
L.A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, pp. 338–353, 1965.
M. Sugeno and G.T. Kang, “Structure identification of fuzzy model,” Fuzzy Sets and Systems, vol. 28, pp. 15–33, 1988.
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.
A. Bardossy and L. Duckstein, Fuzzy Rule-Based Modeling with Application to Geophysical, Biological and Engineering Systems, CRC Press, 1995.
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.
L.X. Wang, Adaptive Fuzzy Systems and Control. Design and Stability Analysis, Prentice-Hall, 1994.
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.
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.
R. Palm, D. Driankov, and H. Hellendoorn, Model Based Fuzzy Control, Springer-Verlag, 1997.
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.
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.
A.E. Gegov and P.M. Frank, “Hierarchical fuzzy control of multivariable systems,” Fuzzy Sets and Systems, vol. 72, pp. 299–310, 1995.
R.R. Yager, “On the construction of hierarchical fuzzy systems model,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 22, pp. 1414–1427, 1992.
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.
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.
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.
C. Karr, “Genetic algorithms for fuzzy controllers,” AI Expert, pp. 26–33, 1991.
C.M. Fonseca and P.J. Fleming, “An overview of evolutionary algorithms in multiobjective optimization,” Evolutionary Computation, vol. 3, pp. 1–16, 1995.
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,.
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.
J.H. Holland, Adaptation in Natural and Artificial Systems, The University of Michigan Press: Ann Arbor, 1975 (The MIT Press, London, 1992).
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.
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.
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.
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Alcalá, R., Benítez, J.M., Casillas, J. et al. Fuzzy Control of HVAC Systems Optimized by Genetic Algorithms. Applied Intelligence 18, 155–177 (2003). https://doi.org/10.1023/A:1021986309149
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DOI: https://doi.org/10.1023/A:1021986309149