Advertisement

Building Simulation

, Volume 6, Issue 4, pp 337–350 | Cite as

A knowledge-aid approach for designing high-performance buildings

  • Fabien Talbourdet
  • Pierre Michel
  • Franck Andrieux
  • Jean-Robert Millet
  • Mohamed El Mankibi
  • Benoit Vinot
Research Article Building Thermal, Lighting, and Acoustics Modeling

Abstract

Today, global warming and the sustained increase in energy prices have led to a quest for energy-efficient buildings among designers and users alike. This has been accompanied by increasingly strict thermal and energy regulations for buildings. In addition to such changes on the energy front, building regulations have also been created or reinforced in other areas, including accessibility, fire safety and seismic risk, alongside the demands of users. The combined effects of these two factors have made building design much more complex. Thus, designers are constantly in search of tools and information that can provide them with ways of designing high-performance buildings for their projects. In response to these needs, we propose an optimization-based, knowledge-aid approach for designing high-performance buildings. This approach is aimed at providing architects and design offices with clear knowledge of their project’s potential (exploration of various options) that will allow them to design the best possible high-performance buildings (in this version of the approach only energy needs and construction cost are assessed). This potential is evaluated by means of the external and internal geometric parameters as well as the energy characteristics of buildings. In this paper, the approach will be applied to an office building in Lyon, France.

Keywords

high-performance building design bioclimatic architecture multicriteria optimization genetic algorithms energy efficiency 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. ADEME (2012). LES BATIMENTS EXEMPLAIRES BBC-PREBAT-BILAN 2007–2012. ADEME: 25.Google Scholar
  2. Bright T, Coley C (2011). Modelling occupant behaviour in passivhaus buildings: Bridging the energy gap. CIBSE Technical Symposium, DeMontfort University, Leicester UK.Google Scholar
  3. Caldas LG (2001). An evolution-based generative design system: Using adaptation to shape architectural form. PhD Desertation, Massachusetts Institute of Technology, USA.Google Scholar
  4. Deb K (2001). Multi-objective Optimization Using Evolutionary Algorithms. Chichester, UK: Wiley.zbMATHGoogle Scholar
  5. Deb K (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6: 182–197.CrossRefGoogle Scholar
  6. Depecker P, Menezo C, Virgone J, Lepers S (2001). Design of buildings shape and energetic consumption. Building and Environment, 36: 627–635.CrossRefGoogle Scholar
  7. Duffy M, Hiller M, Bradley D, Keilholz W, Thornton J (2009). TRNSYS—Features and functionality. In: Proceedings of 11th International IBPSA Conference (pp. 1950–1954), Glasgow, Scotland.Google Scholar
  8. Goldberg DE (1989). Genetic algorithms in Search, Optimization and Machine Learning. Reading, MA, USA: Addison Wesley.zbMATHGoogle Scholar
  9. Lenoir A, Ottenwelter E, Bornarel Q, Hernandez O, Garde F (2010). Etat de l’art des batiments à énergie positive en France. Retour d’expérience et comparaison des consommations énergétiques calculées en phase de conception et mesurées en phase d’utilisation du batiment. IBPSA France 2010. Moret-Veneux Les Sablons, France. (in French)Google Scholar
  10. Magnier L, Haghighat F (2010). Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network. Building and Environment, 45: 739–746.CrossRefGoogle Scholar
  11. Medjdoub B (1996). Méthode de conception fonctionnelle en architecture: une approche CAO basée sur les contraintes: ARCHiPLAN PhD en Productique, Ecole Centrale de Paris. (in French)Google Scholar
  12. Pessenlehner W, Mahdavi A (2003). Building morphology, transparency, and energy performance. In: Proceedings of 8th International IBPSA conference (pp. 1025–1032), Eindhoven, The Netherlands.Google Scholar
  13. Wetter M (2004). Simulation-based building energy optimization. PhD Desertation, University of California at Berkeley, USA.Google Scholar
  14. Wetter M, Wright J (2004). A comparison of deterministic and probabilistic optimization algorithms for nonsmooth simulation-based optimization. Building and Environment, 39: 989–999.CrossRefGoogle Scholar
  15. Zitzler E, Deb K, Thiele L (2000). Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation, 8: 173–195.CrossRefGoogle Scholar
  16. Znouda E, Ghrab-Morcos N, Hadj-Alouane A (2007). Optimization of Mediterranean building design using genetic algorithms. Energy and Buildings, 39: 148–153.CrossRefGoogle Scholar

Copyright information

© Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fabien Talbourdet
    • 1
    • 2
  • Pierre Michel
    • 1
  • Franck Andrieux
    • 2
  • Jean-Robert Millet
    • 3
  • Mohamed El Mankibi
    • 1
  • Benoit Vinot
    • 2
  1. 1.Ecole Nationale des Travaux Publics de l’Etat (ENTPE)Vaulx-en-VelinFrance
  2. 2.Centre Scientifique et Technique du Bâtiment (CSTB)Sophia AntipolisFrance
  3. 3.Centre Scientifique et Technique du Bâtiment (CSTB)Champs-sur-MarneFrance

Personalised recommendations