An interactive expert system for daylighting design exploration

Abstract Architects increasingly use digital tools during the design process, particularly as they approach such complex problems as designing for successful daylighting performance. However, while simulation tools may provide the designer with valuable information, they do not necessarily guide the user toward design changes which will improve performance. This paper proposes an interactive, goal-based expert system for daylighting design, intended for use during the early design phases. The expert system consists of two major components: a daylighting knowledge-base which contains information regarding the effects of a variety of design conditions on resultant daylighting performance, and a fuzzy rule-based decision-making logic which is used to determine those design changes most likely to improve performance for a given design. The system gives the user the ability to input an initial model and a set of daylighting performance goals in the form of illuminance and daylighting-specific glare metrics. The system acts as a “virtual daylighting consultant,” guiding the user toward improved performance while maintaining the integrity of the original design and of the design process itself. Two sets of case studies are presented: first, a comparison of the expert system results to high performing benchmark designs generated with a genetic algorithm; and second, an evaluation of the expert system performance based on varying levels of esthetic constraints. The results of these case studies indicate that the expert system is successful at finding designs with improved performance for a variety of initial geometries and daylighting performance goals.

[1]  Christoph F. Reinhart,et al.  THE 鄂ADAPTIVE ZONE 鈂 阂 A CONCEPT FOR ASSESSING GLARE THROUGHOUT DAYLIT SPACES , 2011 .

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

[3]  M. Rashid,et al.  A Review of the Empirical Literature on the Relationships Between Indoor Environment and Stress in Health Care and Office Settings , 2008 .

[4]  I. G. Capeluto,et al.  Advice tool for early design stages of intelligent facades based on energy and visual comfort approach , 2009 .

[5]  Marilyne Andersen,et al.  A generative facade design method based on daylighting performance goals , 2012 .

[6]  B. Paule,et al.  DIAL : a new computer-based daylighting design tool , 1997 .

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

[8]  Ellen Yi-Luen Do,et al.  Light Pen Sketching light in 3 , 2003 .

[9]  George Luger,et al.  Artificial Intelligence: Structures and Strategies for Complex Problem Solving (5th Edition) , 2004 .

[10]  Jaime M. L. Gagne An interactive performance-based expert system for daylighting in architectural design , 2011 .

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

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

[13]  P. Torcellini,et al.  A Literature Review of the Effects of Natural Light on Building Occupants , 2002 .

[14]  Peter Boyce,et al.  Lighting quality and office work: two field simulation experiments , 2006 .

[15]  Bo Guo,et al.  RetroLite: An artificial intelligence tool for lighting energy-efficiency upgrade , 1993 .

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

[17]  Barbara Cutler,et al.  An intuitive daylighting performance analysis and optimization approach , 2008 .

[18]  Marilyne Andersen,et al.  A daylighting knowledge base for performance-driven facade design exploration , 2011 .

[19]  P. Boyce,et al.  The Benefits of Daylight through Windows , 2003 .

[20]  J. Buckley,et al.  Fuzzy expert systems and fuzzy reasoning , 2004 .