Optimization methodologies for building performance modeling and optimization

Buildings account for approximately 40% of the global energy consumption and 36% of total carbon dioxide emissions. At present, high emphasis is given on the reduction of energy consumption and carbon footprint by optimizing the performance and resource utilization of buildings to achieve sustainable development. Building performance is analyzed in terms of energy performance, indoor environment for human comfort & health, environmental degradation and economic aspects. As for the energy performance analysis, this can be best modeled and optimized by a whole building energy simulation tool coupled with an appropriate optimization algorithm. Building performance optimization problems are inherently multivariate and multi-criteria. Optimization methodologies with different characteristics that are broadly classified as Adaptive, Non-adaptive and Pareto Algorithms can be applied in this regard. The paper discusses the applicability of the aforementioned optimization methodologies in building performance optimization for achieving realistic results. R M P S Bandara is with the Department of Mechanical Engineering, General Sir John Kotelawala Defence University. R A Attalage is with the Department of Mechanical Engineering, University of Moratuwa. (e-mail: bandara@kdu.ac.lk; dvc@uom.lk)

[1]  P. Torcellini,et al.  Automated Multivariate Optimization Tool for Energy Analysis , 2006 .

[2]  Antony D. Radford,et al.  A multicriteria model for building performance and design , 1987 .

[3]  Nm Bouchlaghem,et al.  Optimising the design of building envelopes for thermal performance , 2000 .

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

[5]  H. Jędrzejuk,et al.  Optimization of shape and functional structure of buildings as well as heat source utilisation. Partial problems solution , 2002 .

[6]  Peter Lund,et al.  Multivariate optimization of design trade-offs for solar low energy buildings , 1999 .

[7]  Yolanda Carson,et al.  Simulation optimization: methods and applications , 1997, WSC '97.

[8]  C. L. Gupta,et al.  A systematic approach to optimum thermal design , 1970 .

[9]  Svend Svendsen,et al.  Life cycle cost optimization of buildings with regard to energy use, thermal indoor environment and daylight , 2002 .

[10]  A. Belegundu,et al.  Optimization Concepts and Applications in Engineering , 2011 .

[11]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[12]  Peter J. Fleming,et al.  Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[13]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[14]  Jonathan A. Wright,et al.  Optimization of building thermal design and control by multi-criterion genetic algorithm , 2002 .

[15]  Weimin Wang,et al.  Applying multi-objective genetic algorithms in green building design optimization , 2005 .

[16]  A. B. Templeman,et al.  An approach to the optimum thermal design of office buildings , 1976 .

[17]  Luis I. Gonzalez-Monroy,et al.  Optimization of energy supply systems with simulated annealing: continuous and discrete descriptions , 2000 .

[18]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[19]  H. H. Rosenbrock,et al.  An Automatic Method for Finding the Greatest or Least Value of a Function , 1960, Comput. J..

[20]  Robert Hooke,et al.  `` Direct Search'' Solution of Numerical and Statistical Problems , 1961, JACM.

[21]  L. L. Boyer,et al.  The Framework of an Optimization Model for the Thermal Design of Building Envelopes , 1994 .

[22]  Ashvini Kumar,et al.  Optimum distribution of insulation over various components of an air-conditioned building , 1989 .

[23]  Mohammad S. Al-Homoud Optimum thermal design of office buildings , 1997 .