Genetic Algorithms for Optimization of Building Envelopes and the Design and Control of HVAC Systems

Author:

Caldas L. G.1,Norford L. K.2

Affiliation:

1. Department of Civil Engineering and Architecture, Instituto Superior Te´cnico, Technical University of Lisbon, Av. Rovisco Pais, 1049-001 Lisbon, Portugal

2. Massachusetts Institute of Technology, School of Architecture and Planning, Department of Architecture, 77 Massachusetts Ave., #5-418D, Cambridge, MA 02139-4301

Abstract

Many design problems related to buildings involve minimizing capital and operating costs while providing acceptable service. Genetic algorithms (GAs) are an optimization method that has been applied to these problems. GAs are easily configured, an advantage that often compensates for a sacrifice in performance relative to optimization methods selected specifically for a given problem, and have been shown to give solutions where other methods cannot. This paper reviews the basics of GAs, emphasizing multi-objective optimization problems. It then presents several applications, including determining the size and placement of windows and the composition of building walls, the generation of building form, and the design and operation of HVAC systems. Future work is identified, notably interfaces between a GA and both simulation and CAD programs.

Publisher

ASME International

Subject

Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Reference38 articles.

1. Goldberg, D., 1989, Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley.

2. IEE., 1995, Proc. of 1st Int. Conf. on Genetic Algorithms in Engineering Systems: Innovations and Applications, IEE Conf. Pub. 414.

3. IEE, 1997, Proc. of 2nd Int. Conf. on Genetic Algorithms in Engineering Systems: Innovations and Applications, IEE Conf. Pub. 446.

4. Gero, J., and Radford, A., 1978, “A Dynamic Programming Approach to the Optimum Lighting Problem,” Eng. Optimiz., 3(2), pp. 71–82.

5. Chutarat, A., 2001, “Experience of Light: The Use of an Inverse Method and a Genetic Algorithm in Daylighting Design,” Ph.D. Thesis, Dept. of Architecture, MIT, Cambridge, MA.

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