A generative facade design method based on daylighting performance goals

Successful daylighting design is a complex task which requires the designer to consider numerous design elements and their effects on multiple performance criteria. Facades, in particular, include many variables which may dramatically impact daylighting performance. Genetic algorithms (GAs) are optimization methods which are suitable for searching large solution spaces, such as those presented by design problems. This article presents a GA-based tool which facilitates the exploration of facade designs generated based on illuminance and/or glare objectives. The method allows the user to input an original 3d massing model and performance goals. The overall building form remains the same while facade elements may change. Ten parameters are considered, including materials and geometry of apertures and shading devices. A simple building data model is used to automatically generate a 3d model of each solution. Results from single- and multi-objective case studies are presented to demonstrate a successful goal-driven design exploration process.

[1]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[2]  Andreas K. Athienitis,et al.  Optimization of the performance of double-façades with integrated photovoltaic panels and motorized blinds , 2006 .

[3]  Barbara Cutler,et al.  Goal-Based Daylighting Design Using an Interactive Simulation Method , 2009 .

[4]  Siân A. Kleindienst.  Time-varied daylighting performance to enable a goal-driven design process , 2010 .

[5]  Philipp Geyer,et al.  Component-oriented decomposition for multidisciplinary design optimization in building design , 2009, Adv. Eng. Informatics.

[6]  Luisa Caldas.  Generation of energy-efficient architecture solutions applying GENE_ARCH: An evolution-based generative design system , 2008, Adv. Eng. Informatics.

[7]  Kalmanje Krishnakumar,et al.  Micro-Genetic Algorithms For Stationary And Non-Stationary Function Optimization , 1990, Other Conferences.

[8]  Leslie K. Norford,et al.  A design optimization tool based on a genetic algorithm , 2002 .

[9]  Yuzo Sakamoto,et al.  FACADE DESIGN OPTIMIZATION FOR DAYLIGHT WITH A SIMPLE GENETIC ALGORITHM , 2007 .

[10]  Nader Sadegh,et al.  Real-time optimization of a double-skin facade based on lumped modeling and occupant preference , 2004 .

[11]  Kristina Shea,et al.  Multicriteria Optimization of Paneled Building Envelopes Using Ant Colony Optimization , 2006, EG-ICE.

[12]  Weimin Wang,et al.  An object-oriented framework for simulation-based green building design optimization with genetic algorithms , 2005, Adv. Eng. Informatics.

[13]  Barbara Cutler,et al.  Interactive selection of optimal fenestration materials for schematic architectural daylighting design , 2008 .

[14]  D. Carroll.  GENETIC ALGORITHMS AND OPTIMIZING CHEMICAL OXYGEN-IODINE LASERS , 1996 .

[15]  Carlos A. Coello Coello,et al.  A Micro-Genetic Algorithm for Multiobjective Optimization , 2001, EMO.

[16]  Jonathan A. Wright,et al.  Geometric optimization of fenestration , 2009 .

[17]  Acharawan Chutarat.  Experience of light : the use of an inverse method and a genetic algorithm in daylight design , 2001 .

[18]  Siân A. Kleindienst,et al.  The Adaptation of Daylight Glare Probability to Dynamic Metrics in a Computational Setting , 2009 .

[19]  Peter Lund,et al.  Daylight optimization of multifunctional solar facades , 2000 .

[20]  J. Wienold.  DYNAMIC DAYLIGHT GLARE EVALUATION , 2009 .

[21]  Marilyne Andersen,et al.  Graphical Representation of Climate-Based Daylight Performance to Support Architectural Design , 2008 .

[22]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[23]  Jan Wienold,et al.  Evaluation methods and development of a new glare prediction model for daylight environments with the use of CCD cameras , 2006 .