Computational optimization of housing complexes forms to enhance energy efficiency

    SeyedAli Derazgisou Affiliation
    ; Romualdas Bausys Affiliation
    ; Rima Fayaz Affiliation


This study aimed to consider the field of energy saving in architectural design utilizing computer analysis and calculation. In this analysis, architecture design with an approach to optimizing energy consumption in the design of individual units, complex plan sites, and apartment sets using a computer was studied. Parameters affecting this research include the geometry of units, the arrangement and location relationship of buildings, and the form and height of apartment units. Different plans were produced by utilizing the initial plan of the designer and changing some aspects of it approved by the architectural design using the parametric modeling technique. Utilizing similar logic and a shift in the arrangement of buildings on the site, a variety of options were produced. By selecting existing and pre-designed plans, the optimal form was produced by computer. After computer-simulating each option, the energy analysis process was started for each building design. In the optimization process for each of the three designs, a genetic algorithm was used to achieve the optimal solution. After accomplishing the various stages of optimization, the final option compared with the initial design had reductions in energy consumption of 21% in plan design, 2% in site plan design, and 26% in apartment units form design. It should be noted that the processes of simulation and optimization were performed in the context of a continuous algorithm and by utilizing parametric tools that reduced the duration of this process.

Keyword : algorithmic design, parametric modelling, optimization, energy saving, Grasshopper

How to Cite
Derazgisou, S., Bausys, R., & Fayaz, R. (2018). Computational optimization of housing complexes forms to enhance energy efficiency. Journal of Civil Engineering and Management, 24(3), 193-205.
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May 25, 2018
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Ali, M.; Vukovic, V.; Sahir, M. H.; Fontanella, G. 2013. Energy analysis of chilled water system configurations using simulation-based optimization, Energy and Buildings 59: 111–122.

ASHRAE. 2005. Fenestration, Chapter 31, in Fundamentals handbook. American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE).

Cellura, M.; Ciulla, G.; Lo Brano, V.; Orioli, A.; Campanella, L.; Guarino, F.; Nardi Cesarini, D. 2011. The redesign of an Italian building to reach net zero energy performances: a case study of the SHC Task 40 – ECBCS Annex 52 (ML-11-C040), ASHRAE Transactions 117(2).

Chiras, D. 2002. The solar house: Passive heating and cooling. White River Junction, VT: Chelsea Green Publishing.

Djuric, N.; Novakovic, V.; Holst, J.; Mitrovic, Z. 2007. Optimization of energy consumption in buildings with hydronic heating systems considering thermal comfort by use of computer-based tools, Energy and Buildings 39(4): 471–477.

Eve Lin, S.-H.; Gerber, D. J. 2014. Designing-in performance: A framework for evolutionary energy performance feedback in early stage design, Automation in Construction 38: 59–73.

Ghrab-Morcos, N. 2005. CHEOPS: a simplified tool for thermal assessment of Mediterranean buildings in both hot and cold seasons, Energy and Buildings 37: 651–662.

Givoni, B. 1989. Urban design in different climates. World Meteorological Organization WMO/TD. No. 346.

Givoni, B. 1998. Climate considerations in building and urban design. New York: John Wiley & Sons.

Graduate School of Design at Harvard University. 2014. DIVA for Grasshopper The plug-in was initially developed at the Graduate School of Design at Harvard University and is distributed by Solemma LLC [online], [cited 10 March 2018]. Available from Internet:

Hachem, C.; Athienitis, A.; Fazio, P. 2011a. Parametric investigation of geometric form effects on solar potential of housing units, Solar Energy 85(9): 1864–1877.

Hachem, C.; Athienitis, A.; Fazio, P. 2011b. Design of solar optimized neighborhood, in Proceedings of the ASHRAE 2011 Annual Conference, 25–29 June 2012, Montreal, Canada.

Hachem, C.; Athienitis, A.; Fazio, P. 2012. Evaluation of energy supply and demand in solar neighborhood, Energy and Buildings 49: 335–347.

Khabazi, Z. 2012. Algorithmic architecture paradigm. Ketabkade Kasra Press (in Persian).

Knowles, R. L. 1981. Sun rhythm form. Cambridge, Massachusetts: The MIT Press.

Lin, B.; Yu, Q.; Li, Z.; Zhou, X. 2013. Research on parametric design method for energy efficiency of green building in architectural scheme phase, Frontiers of Architectural Research 2(1): 11–22.

Loutzenhiser, P. G.; Manz, H.; Felsmann, C.; Strachan P. A.; Frank, T.; Maxwell, G. M. 2007. Empirical validation of models to compute solar irradiance on inclined surfaces for building energy simulation, Solar Energy 81(2): 254–267.

Loutzenhiser, P. G.; Manz, H.; Moosberger, S.; Maxwell, G. 2009. An empirical validation of window solar gain models and the associated interactions, International Journal of Thermal Sciences 48(1): 85–95.

Magnier, L.; Haghighat, F. 2010. Multi objective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network, Building and Environment 45(3): 739–746.

Martin, L. 1967. Architect’s approach to architecture, RIBA Journal 74(5): 191–200.

Mossolly, M.; Ghali, K.; Ghaddar, N. 2009. Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm, Energy and Buildings 34(1): 58–66.

Nguyen, A.-T.; Reiter, S.; Rigo, P. 2014. A review on simulation-based optimization methods applied to building performance analysis, Applied Energy 113: 1043–1058.

Oke, T. R. 1978. Boundary layer climates. London: Methuen.

Okeil, A. 2010. A holistic approach to energy efficient building forms, Energy and Buildings 42: 1437–1444.

Olgyay, V. 1963a. Design with climate. Princeton, NJ: Princeton University Press.

Olgyay, V. 1963b. Design with climate: Bioclimatic approach to architectural regionalism. Princeton, NJ: Princeton University Press.

Peippo, K.; Lund, P. D.; Vartiainen, E. 1999. Multivariate optimization of design trade-offs for solar low energy buildings, Energy and Buildings 29: 189–205.

Singh, M. K.; Mahapatra, S.; Atreya, S. K. 2007. Development of bio-climatic zones in north-east India, Energy and Buildings 39: 1250–1257.

Steemers, K. 2003. Energy and the city: density, buildings and transport, Energy and Buildings 5(1): 3–14.

The weather network [online], [cited 10 Jan 2014]. Available from Internet: days/cl7025250

Wang, W.; Zmeureanu, R.; Rivard, H. 2005. Applying multi-objective genetic algorithms in green building design optimization, Building and Environment 40(11): 1512–1525.

Wright, J. A.; Loosemore, H. A.; Farmani, R. 2002. Optimization of building thermal design and control by multi-criterion genetic algorithm, Energy and Buildings 34(9): 959–972.

Yang, C.; Li, H.; Rezgui, Y.; Petri, I.; Yuce, B.; Chen, B.; Jayan, B. 2014. High throughput computing based distributed genetic algorithm forbuilding energy consumption optimization, Energy and Buildings 76: 92–101.

Znouda, E.; Ghrab-Morcos, N.; Hadj-Alouane, A. 2007. Optimization of Mediterranean building design using genetic algorithms, Energy and Buildings 39: 148–153.