Dual-purpose model of energy consumption-construction cost to evaluate the construction methods of the outer shell of residential buildings: A real case study

Document Type : Research Paper

Authors

1 Department of Industrial Management, Islamic Azad University, Science and Research, Najafabad branch, ,Esfahan, Iran

2 School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract

The outer shell is the primary protection of the building against adverse weather conditions and determines the heat exchange rate to the environment. Evaluating and optimizing the outer shell design of residential buildings due to multiple and conflicting criteria such as energy consumption, costs, and environmental impacts is a multi-objective challenge. In this paper, a bi-objective model is presented to evaluate different methods of constructing the outer shell of residential buildings to reduce energy consumption at the lowest possible cost significantly. So that, minimizing the heat transfer from the outer shell as a function of the energy target and minimizing the cost of fabricating the components of the outer shell as a function of the cost and the augmented epsilon constraint method are used to solve the model and determine Pareto’s solutions. The results show that by determining the appropriate thickness and density of the walls and the appropriate ratio of walls’ permeable surface while spending reasonable costs, it is possible to reduce required energy for cooling and heating the house.

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Albertini, F., Gomes, L. P., Grondona, A. E. B., & Caetano, M. O. (2021). Assessment of environmental performance in building construction sites: Data envelopment analysis and Tobit model approach. Journal of Building Engineering, 44, 102994.
Araújo, C., Almeida, M., Bragança, L., & Barbosa, J. A. (2016). Cost–benefit analysis method for building solutions. Applied energy, 173, 124-133.
Askaripoor, T., Shirali, G. A., Yarahmadi, R., & Kazemi, E. (2016). The role of the implementation of national building regulations in the fire safety improvement of industrial structures. Iranian journal of health, safety and environment, 3(4), 633-637.
Azadeh, A., Rezaei-Malek, M., Evazabadian, F., & Sheikhalishahi, M. (2015). Improved design of CMS by considering operators decision-making styles. International Journal of Production Research, 53(11), 3276-3287.
Beltrán, R. D., & Martínez-Gómez, J. (2019). Analysis of phase change materials (PCM) for building wallboards based on the effect of environment. Journal of Building Engineering, 24, 100726.
Chowdhury, M. M. H., & Quaddus, M. A. (2015). A multiple objective optimization based QFD approach for efficient resilient strategies to mitigate supply chain vulnerabilities: The case of garment industry of Bangladesh. Omega, 57, 5-21.
Delgarm, N., Sajadi, B., Kowsary, F., & Delgarm, S. (2016). Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO). Applied energy, 170, 293-303.
Diakaki, Christina, Grigoroudis, Evangelos, Kabelis, Nikos, Kolokotsa, Dionyssia, Kalaitzakis, Kostas, & Stavrakakis, George. (2010). A multi-objective decision model for the improvement of energy efficiency in buildings. Energy, 35(12), 5483-5496.
Esmaili, Masoud, Amjady, Nima, & Shayanfar, Heidar Ali. (2011). Multi-objective congestion management by modified augmented ε-constraint method. Applied Energy, 88(3), 755-766.
Gamayunova, O, Petrichenko, M, & Mottaeva, A. (2020). Thermotechnical calculation of enclosing structures of a standard type residential building. Paper presented at the Journal of Physics: Conference Series.
Hamdy, Mohamed, Hasan, Ala, & Siren, Kai. (2013). A multi-stage optimization method for cost-optimal and nearly-zero-energy building solutions in line with the EPBD-recast 2010. Energy and Buildings, 56, 189-203.
Ho, Yu-Feng, Chang, Ching-Chih, Wei, Chao-Cheng, & Wang, Hsiao-Lin. (2014). Multi-objective programming model for energy conservation and renewable energy structure of a low carbon campus. Energy and buildings, 80, 461-468.
Hwang, C-L, & Masud, Abu Syed Md. (2012). Multiple objective decision making—methods and applications: a state-of-the-art survey (Vol. 164): Springer Science & Business Media.
Incropera, F. P., DeWitt, D. P., Bergman, T. L., & Lavine, A. S. (1996). Fundamentals of heat and mass transfer (Vol. 6). New York: Wiley.
Karmellos, Marias, Kiprakis, Aristides, & Mavrotas, George. (2015). A multi-objective approach for optimal prioritization of energy efficiency measures in buildings: Model, software and case studies. Applied Energy, 139, 131-150.
Kaya, İhsan, Çolak, Murat, & Terzi, Fulya. (2019). A comprehensive review of fuzzy multi criteria decision making methodologies for energy policy making. Energy Strategy Reviews, 24, 207-228.
Li, Lingyan, Wang, Yao, Wang, Mengmeng, Hu, Wei, & Sun, Yongkai. (2021). Impacts of multiple factors on energy consumption of aging residential buildings based on a system dynamics model--Taking Northwest China as an example. Journal of Building Engineering, 44, 102595.
Lienhard, I. V., & John, H. (2005). A heat transfer textbook. phlogiston press.
Mavrotas, George. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied mathematics and computation, 213(2), 455-465.
Motawa, Ibrahim, Elsheikh, Asser, & Diab, Esraa. (2021). Energy Performance Analysis of Building Envelopes. Journal of Engineering, Project, and Production Management, 11(3), 196.
National Building Regulations of Iran, Topic 19, Energy Conservation, 2010
Peng, Changhai, Huang, Lu, Liu, Jianxun, & Huang, Ying. (2015). Energy performance evaluation of a marketable net-zero-energy house: Solark I at Solar Decathlon China 2013. Renewable Energy, 81, 136-149.
Trach, Roman, Trach, Yuliia, & Lendo-Siwicka, Marzena. (2021). Using ANN to Predict the Impact of Communication Factors on the Rework Cost in Construction Projects. Energies, 14(14), 4376.
U.S. Department of Energy (DOE). 2009 Buildings Energy Data Book. Prepared for U.S. Department of Energy Office of Energy Efficiency and Renewable Energy by D&R International, Ltd. Silver Spring, MD. October 2009. http://buildingsdatabook.eren.doe.gov/