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Building Simulation 2013, 6(4): 337-350 https://doi.org/10.1007/s12273-013-0122-y
Research Article | Issue | Published: 16 May 2013

A knowledge-aid approach for designing high-performance buildings

Show Author's Information Fabien Talbourdet1,2( ),  Pierre Michel1,  Franck Andrieux2,  Jean-Robert Millet3,  Mohamed El Mankibi1,  Benoit Vinot2
Ecole Nationale des Travaux Publics de l’Etat (ENTPE), Vaulx-en-Velin, France
Centre Scientifique et Technique du Bâtiment (CSTB), Sophia Antipolis, France
Centre Scientifique et Technique du Bâtiment (CSTB), Champs-sur-Marne, France
Keywords:
energy efficiency, genetic algorithms, bioclimatic architecture, high-performance building design, multicriteria optimization
Cite this article:
Talbourdet F, Michel P, Andrieux F, et al. A knowledge-aid approach for designing high-performance buildings. Building Simulation, 2013, 6(4): 337-350. https://doi.org/10.1007/s12273-013-0122-y
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Abstract Full text About this article

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.


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About this article

A knowledge-aid approach for designing high-performance buildings

Show Author's information Fabien Talbourdet1,2( ), Pierre Michel1, Franck Andrieux2, Jean-Robert Millet3, Mohamed El Mankibi1, Benoit Vinot2
Ecole Nationale des Travaux Publics de l’Etat (ENTPE), Vaulx-en-Velin, France
Centre Scientifique et Technique du Bâtiment (CSTB), Sophia Antipolis, France
Centre Scientifique et Technique du Bâtiment (CSTB), Champs-sur-Marne, France

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

References(16)

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

Publication history

Received: 13 July 2012
Revised: 21 January 2013
Accepted: 18 February 2013
Published: 16 May 2013
Issue date: December 2013

Copyright

© Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2013
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