A study on the payback period of building energy consumption optimization in different situations

Document Type : Research Paper


Faculty of Architecture and Urban Planning, Shahid Beheshti University, Tehran, Iran


Buildings are the main pillars of social and economic development of countries and they consume a huge amount of energy and natural resources. This energy consumption is 30-50% on average. The present study aims to investigate the effect of the locations of buildings around neighboring buildings and pathways on the payback period of optimization. The research scope includes common residential apartments in Tehran. According to the research method, we consider 6 similar residential blocks in different lighting situations. Using simulation in Design Builder software, we calculate their energy consumption and then optimize their energy consumption. The optimization variables are as follows: the material of the outer wall (clay or LECA) and the façade (stone or brick), the type of window glass (plain or low-emissivity), the type of gas between the layers of the window glass (air or argon), and the ratio of the window to the surface of various directions the building. The optimization goals include the minimization of energy consumption and construction cost. Design Builder software and genetic algorithm are used to optimize the variables. After optimization, the 6 selected optimized blocks are then calculated in the simulation software and their energy consumption is calculated and compared with the results before optimization. The research results indicate that the mean reduction in energy consumption is 77% in the northern blocks, 65.2% in the southern blocks, and 71% in all blocks, and the optimization results in the northern blocks are about 12% better than the southern blocks. Given the proposed optimization changes, we calculate and compare the increase in the construction cost of each block. The results indicate that the mean increase in the construction cost is 1.6%  in the northern blocks, 2.7% in the southern blocks, and 2.2%  in all blocks, and the increase in the costs of building northern blocks is about 1.1% less than southern blocks. According to the prices of electricity and gas in Iran, we measure the annual energy cost saving and the results indicate that the payback period of optimization is about 7.7 years in the northern blocks, 13.8 years in the southern blocks, and 10.8 years in average, and the calculations indicate that if the mean global energy prices prevail in Iran, this time reduces to 6 months.


[1] A. Alaidroos and M. Krarti, Optimal design of residential building envelope systems in the Kingdom of Saudi Arabia, Energy Build. 86 (2015), 104–117.
[2] S. Arabzadeh and S. Kazemzadeh, A study on the effective parameters in energy consumption in the residential sector in Iran, 4th Conf. Optim. Fuel Consump. Build. Iran, 2005.
[3] M. Asif, T. Muneer and R. Kelley, Life cycle assessment: a case study of a dwelling home in Scotland, Build. Envir. 42 (2007), no. 3, 1391–1394.
[4] S. Attia, State of the Art of Existing Early Design Simulation Tools for Net Zero Energy Buildings: A Comparison of Ten Tools, Universit´e catholique de Louvain, Louvain La Neuve, Belgium, 2011.
[5] F. Bagheri and V. Makarizadeh, Analysis of the effectiveness of light of pathways in improving energy consumption and producing polluting gases in residential buildings of Iran, Energy Adv. 1 (2018), no. 1, 64–67.
[6] S. M. Bambrook, A. B. Sproul and D. Jacob, Design optimization for a low energy home in Sydney, Energy Build. 43 (201), no. 7, 1702–1711.
[7] M. Baum and U. G. B. Council, Green building research funding: an assessment of current activity in the United States, US Green Building Council, Washington, DC, 2007.
[8] M. Bideli, H. Madi, J. Soheili and K. Rahbari-Manesh, Assessment of the intermediate cavity impact on the cooling energy performance of the multi-story double-skin facade in hot and humid climate (Kish Island), Armanshahr Architect. Urban Dev. J. 13 (2020), no. 30, 19–29.
[9] L. Caldas, Generation of Energy-Efficient Patio Houses: Combining GENE ARCH and a Marrakesh Medina Shape Grammar, AAAI Spring Symposium: Artificial Intelligence and Sustainable Design, 2011.
[10] L. Caldas, Generation of energy-efficient architecture solutions applying GENE ARCH: An evolution-based generative design system, Adv. Engin. Inf. 22 (2008), no. 1, 59–70.
[11] L.G. Caldas and L.K. Norford, Genetic Algorithms for Optimization of Building Envelopes and the Design and Control of HVAC Systems, J. Solar Energy Engin. 125 (2003), no. 3, 343–352.
[12] J. Carreras, D. Boer, G. Guill´en-Gos´albez, M. CabezaL, Medrano and L. Jim´enez, Multi-objective optimization of thermal modelled cubicles considering the total cost and life cycle environmental impact, Energy Build. 88 (2015), no. 1, 335–346.
[13] F. Chantrelle, H. Lahmidi, W. Keilholz, M. El Mankibi and P. Michel, Development of a multi-criteria tool for optimizing the renovation of buildings, Appl. Energy 88 (2011), 1386–1394.
[14] N. D’Cruz, A.D. Radford and J.S. Gero, A Pareto optimization problem formulation for building Performance and design, Engin. Optim. 17 (1983), no. 1, 17–33.
[15] N. Djuric, V. Novakovic, J. Holst and Z. Mitrovic, Optimization of energy consumption in buildings with hydronic heating systems considering thermal comfort by use of computer-based tools, Energy Build. 39 (2007), no. 4, 471–477.
[16] A. Ebrahimpour and Y. Karimi Vahed, Appropriate methods for optimizing energy consumption in a university building in Tabriz, Modares Mech. Engin. J. 12 (2012), no. 4, 91–104.
[17] EIA, EIA, Eurostat, and BRE. 2004.
[18] Energy Efficiency Organization of Iran, Energy Balance Sheet, 2014.
[19] B. Farhanieh and S. Sattari, Simulation of energy saving in Iranian buildings using integrative modeling forinsulation, Renewable Energy 31 (2006), 417–425.
[20] I. Gaetani, P.-J. Hoes and J. L. Hensen, Occupant behavior in building energy simulation: Towards a fit-forpurpose modeling strategy, Energy Build. 121 (2016), 188-204.
[21] Sh. Ghaffari Jabbari and A. Saleh, Housing design solutions to optimize energy consumption in Tehran, Int. Conf. Civil Engin. Architecture Sustain. Dev. Tabriz, 2013.
[22] M. Ghiaei and A. Hosseinpour Hajjar, The relationship between energy consumption and opening ratio in high-rise office buildings, J. Architecture Sustain. Urban Dev., 2013.
[23] M.M. Ghiyaee M. Mahdavi Niya, M. Tahbaz and M. Mofidi Shemirani, A methodology for selecting applied energy simulation tools in the field of architecture, Hoviate Shahr J. 7 (2013), no. 13, 45–55.
[24] M. Hamdy, A. Nguyen and J. Hensen, Performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems, Energy Build. 121 (2016), 57–71.
[25] R. Heijungs and R. Frischknecht, A special view on the nature of the allocation problem, Int. J. Life Cycle Assess. 3 (1998), no. 5, 321–332.
[26] P. Hose, J. Hensen, M. Loomans, B. de Vries and D. Bourgeois, User behavior in whole building simulation, Energy Build. 41 (2009), no. 3, 295–302.
[27] O.T. Karaguzel, R. Zhang and K.P. Lam, Coupling of whole-building energy simulation and multi-dimensional numerical optimization for minimizing the life cycle costs of office buildings, Build. Simul. 72 (2014), no. 2, 111–121.
[28] A. Karimpour, D. Diba and A. Etessam, Economic analysis and assessing energy performance of simulationpowered optimal window Type and rindow to wall ratio for residential buildings in Tehran, Hoviate Shahr J. 13 (2019), no. 3, 19–34.
[29] A. Kusiak, G. Xu and M. Krarti, Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm, Energy 36 (2011), 5935–5943.
[30] L. Magnier and F. Haghighat, Multiobjective optimization of building design using TRNSYS, Build. Envir. 45 (2010), 739–746.
[31] M.J. Mahdavinejad and S. Masoudi Tonekaboni, Self-shading and high performance architecture, case studies: Configuration of contemporary buildings of Tehran, Armanshahr Architect. Urban Dev. J. 11 (2015), no. 25, 201–208.
[32] Ministry of Energy website
[33] S.M. Mirhashemi, S.M. Shapourian and Z. Ghiabaklou, A new method in optimizing single glazing windows, Fine Arts J. 43 (2010), 43–48.
[34] E. Naboni, A. Maccarini, I. Korolija and Y. Zhang, Comparison of conventional, parametric and evolutionary optimization approach for the architectural design of nearly zero energy buildings, Proc. Thirteenth Int. IBPSA Conf., 2013.
[35] N. Nasrollahi, S. Abdollahzadeh and S. Litkohi, The effect of atrium on indoor environment, occupant’s thermal comfort and energy consumption in office buildings, case study: Tehran, Armanshahr Architect. Urban. J. 10 (2018), no. 21, 125–138.
[36] P. Penna, A. Prada, F. Cappelletti and A. Gasparella, Multi-objectives optimization of Energy Efficiency Measures in existing buildings, Energy Build. 92 (2015), 57–69.
[37] B. Raji, M. J. Tenpierik and A. van den Dobbelste, An assessment of energy-saving solutions for the envelope design of high-rise buildings in temperate climates: A case study in the Netherlands, Energy Build. 124 (2016), 210–221.
[38] T. Ramesh, P. Prakasha and K. Shukla, Life cycle energy analysis of buildings: An overview, Energy Build. 42 (2010), 1592–1600.
[39] H. Ramin, P. Hanafizadeh, T. Ehterami and M. A. Akhavan-Behabadi, Life cycle-based multi-objective optimization of wall structures in the climate of Tehran, Adv. Build. Energy Res. 13 (2019), no. 1, 18–31.
Volume 14, Issue 4
April 2023
Pages 171-191
  • Receive Date: 22 June 2022
  • Revise Date: 03 August 2022
  • Accept Date: 09 August 2022