Designing-in performance: Evolutionary energy performance feedback for early stage design

Proceedings of BS 2013: 13th Conference of the International Building Performance Simulation Association

Volumn , Issue , 2013, Pages 386-393

  Lin, Shih Hsin Eve ^a   Gerber, David Jason ^a  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BUILDING LIFE CYCLE; DESIGN PROCESS; EARLY DESIGNS; EARLY STAGE DESIGNS; ENERGY PERFORMANCE; GEOMETRIC COMPLEXITY; MULTIDISCIPLINARY DESIGN OPTIMIZATION; SOLUTION SPACE;

DESIGN AIDS; ENERGY EFFICIENCY;

DESIGN;

EID: 84886657598     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. AIAA 1991. Current state of the art on multidisciplinary design optimization (MDO), Washington D.C., USA, American Institute of Aeronautics and Astronautics.
2. Asadi, E., da Silva, M.G., Antunes, C.H. & Dias, L. 2012. A multi-objective optimization model for building retrofit strategies using TRNSYS simulations, GenOpt and MATLAB. Building and Environment, 56, 370-378.
3. Attia, S., Hensen, J.L.M., Beltrán, L. & De Herde, A. 2012. Selection criteria for building performance simulation tools: contrasting architects' and engineers' needs. Journal of Building Performance Simulation, 5, 155-169.
4. Augenbroe, G. 2002. Trends in building simulation. Building and Environment, 37, 891-902.
5. Autodesk. 2013a. Autodesk Green Building Studio: Web-Based Energy Anlaysis Software [Online]. Availablehttp://usa.autodesk.com/greenbuilding- studio/[Accessed 6 April 2013].
6. Autodesk. 2013b. Autodesk Revit [Online]. Available: http://www.autodesk. com/products/autodeskrevit-family/overview [Accessed 6 April 2013].
7. Crawley, D.B., Hand, J.W., Kummert, M. & Griffith, B.T. 2008. Contrasting the capabilities of building energy performance simulation programs. Building and Environment, 43, 661- 673.
8. Flager, F. & Haymaker, J. A comparison of multidisciplinary design, analysis and optimization processes in the building construction and aerospace industries. In: Smith, I., ed. 24th W78 Conference on Bringing ITC knowledge to work, 26-29 June 2007 2007 Maribor, Slovenia. 625-630.
9. Flager, F., Welle, B., Bansal, P., Soremekun, G. & Haymaker, J. 2009. Multidisciplinary process integration and design optimization of a classroom building. Information Technology in Construction, 14, 595-612.
10. Fonseca, C.M. & Fleming, P.J. Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization. In: Forrest, S., ed. The Fifth International Conference on Genetic Algorithms, 17-21 July 1993 1993 San Mateo, CA, USA. Morgan Kaufmann, 416-423.
11. Gerber, D.J., Lin, S.-H.E., Pan, B.P. & Solmaz, A.S. Design optioneering: Multi-disciplinary design optimization through parameterization, domain integration and automation of a genetic algorithm. In: Nikolovska, L. & Attar, R., eds. SimAUD 2012, 26-30 March 2012 2012 Orlando, Florida, USA. 23-30.
12. Geyer, P. 2009. Component-oriented decomposition for multidisciplinary design optimization in building design. Advanced Engineering Informatics, 23, 12-31.
13. Holzer, D. 2010. Optioneering in collaborative design practice. International Journal of Architectural Computing, 8, 165-182.
14. 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.
15. Microsoft. 2013. Excel - Analyze [Online]. Available: http://office.microsoft.com/en-us/excel/[Accessed 6 April 2013].
16. Oxman, R. 2008. Performance-based design: Current practices and research issues. International Journal of Architectural Computing, 6, 1-17.
17. Radford, A.D. & Gero, J.S. 1980. Tradeoff diagrams for the integrated design of the physical environment in buildings. Building and Environment, 15, 3-15.
18. Sobieszczanski-Sobieski, J. 1993. Multidisciplinary design optimization: An emerging new engineering discipline. Hampton, Virginia: Langley Research Center, NASA.
19. Suga, K., Kato, S. & Hiyama, K. 2010. Structural analysis of Pareto-optimal solution sets for multi-objective optimization: An application to outer window design problems using multiple objective genetic algorithms. Building and Environment, 45, 1144-1152.
20. Toth, B., Salim, F., Burry, J., Frazer, J.H., Drogemuller, R. & Burry, M. 2011. Energyoriented design tools for collaboration in the cloud. International Journal of Architectural Computing.
21. Wright, J.A., Loosemore, H.A. & Farmani, R. 2002. Optimization of building thermal design and control by multi-criterion genetic algorithm. Energy and Buildings, 34, 959-972.