×
Home Current Archive Editorial board
News Contact
Professional paper

OPTIMAL ENERGY EFFICIENT BUILDING DESIGN USING IMPROVED EVOLUTIONARY ALGORITHM

By
Goran Pejičić ,
Goran Pejičić

University UNION Nikola Tesla, Beograd, Serbia

Miloš Vrbanac ,
Miloš Vrbanac

University UNION Nikola Tesla, Beograd, Serbia

Milenko Vukadinović
Milenko Vukadinović

Vukadinovic Inzenjering, D.O.O. Sid, Sid, Serbia

Abstract

Considering energy efficiency and sustainable development on one side and economy aspects on the other, optimal building design has to meet two confronted demands: to minimize total cost of the construction and to minimize energy consumption, which is usually obtained by implementation of expensive insulation and equipment. This paper presents solving methodology using evolutionary algorithm improved by introducing the tabu search module and combined with EnergyPlus software. The results are demonstrated on the example of optimization of insulation materials and orientation angle of a given building, confirming that proposed methodology successfully meets design demands.

References

1.
Grierson DE, Khajehpour S. Method for Conceptual Design Applied to Office Buildings. Vol. 16. 2002. p. 83–103.
2.
Caldas LG, Norford LK. Genetic Algorithms for Optimization of Building Envelopes and the Design and Control of HVAC Systems. Vol. 125. 2003. p. 343–51.
3.
Jourdan CL, Basseur M, Talbi E. Hybridizing Exact Method and Metaheuristics: A Taxonomy. European Journal of Operational Research. 2008;
4.
Talbi DE. A Taxonomy of Hybrid Metaheuristics. J. of Heur. 8(5), 541-564.
5.
Xhafa F. A Hybrid Evolutionary Heuristic for Job Scheduling in Computational Grids, chapter 10. Vol. 75, Springer Series: Studies in Comp. Intell.
6.
Pitman М, King A. Engineering solutions to optimise the design of carbon-neutral tall office buildings. In Proc. International Conference on Solutions for a Sustainable Planet. 2009.
7.
Diakaki C, Grigoroudis E, Kolokotsa D. Towards a multi-objective optimization approach for improving energy effiency in buildings. Energy and Buildings, 40(9):1747 – 1754. 2008.
8.
Caldas L. Generation of energy-effient architecture solutions applying gene arch: An evolutionbased generative design system. Advanced Engineering Informatics, 22(1):59 – 70. 2008.
9.
Optimization of building thermal design and control by multi-criterion genetic algorithm. Energy and Buildings, 34(9):959 – 972. 2002.
10.
Angelov P, Zhang Y, Wright J, Hanby V, Buswell R. Automatic design synthesis and optimization of component-based systems by evolutionary algorithms. In E. Cant´u-Paz et al, editor, Proc. GECCO, volume 2724 of LNCS, pages 1938–1950. Springer. 2003.
11.
Holland J. Adaptation in Natural and Artificial Systems, MITPress, Cambridge, MA. 1992.
12.
Goldberg D. Genetic Algorithms in Search, Optimisation and Machine Learning, AddisonWesley, Reading, MA. 1989.
13.
Wehrens R, Buydens L. Trends Anal. Vol. 17, Chem. p. 193–203.
14.
Glover F, Laguna М. Tabu Search. Kluwer Academic Publishers; 2000.
15.
Getting started with EnergyPlus. Technical report, Lawrence Berkeley National Laboratory. 2010.

Citation

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 

Article metrics

Google scholar: See link

The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.