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Multicriteria Genetic Tuning for the Optimization and Control of HVAC Systems

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Applied Decision Support with Soft Computing

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 124))

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Abstract

This work presents the use of genetic algorithms for the optimization and control of Heating, Ventilating and Air Conditioning (HVAC) systems developing smartly tuned fuzzy logic controllers for energy efficiency and overall performance of these systems.

An optimum operation of the HVAC systems is a necessary condition for minimizing energy consumptions and optimizing indoor comfort in buildings. 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 problems, three efficient genetic tuning strategies, considering different multicriteria approaches, have been presented and tested in two real test sites (buildings) obtaining satisfactory results.

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Author information

Authors and Affiliations

  1. Dept. of Computer Science, University of Jaén, E-23071, Jaén, Spain

    Rafael Alcalá

  2. Dept. of Computer Science and A.I., University of Granada, E-18071, Granada, Spain

    José Manuel Benítez, Jorge Casillas, Juan Luis Castro, Oscar Cordón, Antonio González, Francisco Herrera & Raúl Pérez

Authors
  1. Rafael Alcalá
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  2. José Manuel Benítez
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  3. Jorge Casillas
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  4. Juan Luis Castro
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  5. Oscar Cordón
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  6. Antonio González
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  7. Francisco Herrera
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  8. Raúl Pérez
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Editor information

Editors and Affiliations

  1. Faculty of Engineering, Royal Melbourne Institute of Technology, GPO Box 2476V, 3001, Melbourne, VIC, Australia

    Xinghuo Yu

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

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Alcalá, R. et al. (2003). Multicriteria Genetic Tuning for the Optimization and Control of HVAC Systems. In: Yu, X., Kacprzyk, J. (eds) Applied Decision Support with Soft Computing. Studies in Fuzziness and Soft Computing, vol 124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-37008-6_14

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  • DOI: https://doi.org/10.1007/978-3-540-37008-6_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-53534-5

  • Online ISBN: 978-3-540-37008-6

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