Effectiveness of standalone simulation-based optimization software in
optimizing the life cycle cost of residential buildings

Ozorhon, Beliz; Yigit, Sadik

doi:10.31462/jcemi.2021.04210228

Effectiveness of standalone simulation-based optimization software in optimizing the life cycle cost of residential buildings

Sadık YİĞİT, (Işık Üniversitesi, İnşaat Mühendisliği Bölümü, İstanbul, Türkiye)

Beliz OZORHON (Boğaziçi Üniversitesi, İnşaat Mühendisliği Bölümü, İstanbul, Türkiye)

Journal of Construction Engineering, Management & Innovation (Online)

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Yıl: 2021 Cilt: 4 Sayı: 4 Sayfa Aralığı: 210 - 228 Metin Dili: İngilizce DOI: 10.31462/jcemi.2021.04210228 İndeks Tarihi: 26-05-2022

Effectiveness of standalone simulation-based optimization software in optimizing the life cycle cost of residential buildings

Öz:

Designers aim to build nearly zero energy buildings and positive energy buildings to comply with regulations. However, due to many variables affecting the energy performance of buildings, energy-efficient building design is a challenging task. Among the proposed methods, simulation-based systems are promising. The proposed simulation-based systems are not suitable for the construction sector because of the long optimization periods. The primary goal of this study is to emphasize the necessity of standalone software packages in solving usability problems and to provide a tool for designers and architects to incorporate into their daily works. To demonstrate the advantages of standalone software a test study was conducted to find a cost-optimal configuration for a typical residential building. In addition, the obtained cost-optimal design was compared to the energy-optimal design obtained in previous studies and it was seen that the outcomes are in parallel with the results of previous studies. It was observed that the optimum insulation thickness obtained from the case study is significantly higher than the limiting values in the national regulation. The results of the parametric analysis demonstrated that wall type, window area, and window type have the highest influence on thermal performance. The results of the study have confirmed that stand-alone software performs optimizations faster overcomes the shortcomings of simulation-based optimization systems comprising integrated multiple software packages.

Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık

[1] Outlook AE. Energy Information Administration, US Government. 2012.
[2] Conti J, Holtberg P, Diefenderfer J, LaRose A, Turnure JT, Westfall L. International energy outlook 2016 with projections to 2040. USDOE Energy Information Administration (EIA), Washington, DC (United States …; 2016.
[3] Topak FA, Tokdemir OB, Pekeriçli MK, Tanyer AM (2019) Sustainable construction in Turkish higher education context. Journal of Construction Engineering, Management & Innovation 2(1): 40- 47.
[4] Castro-Lacouture D, Sefair JA, Flórez L, Medaglia AL (2009) Optimization model for the selection of materials using a LEED-based green building rating system in Colombia. Building and Environment 44(6): 1162-1170.
[5] Bahadır Ü, Thomollari X, Toğan V (2018) Evaluation of energy-cost efficient design alternatives for residential buildings. Journal of Construction Engineering, Management & Innovation 1(1): 43-54.
[6] Ferrara M, Fabrizio E, Virgone J, Filippi M (2014) A simulation-based optimization method for costoptimal analysis of nearly zero energy buildings. Energy and Buildings 84: 442-457.
[7] Nguyen AT, Reiter S (2014) Passive designs and strategies for low-cost housing using simulationbased optimization and different thermal comfort criteria. Journal of Building Performance Simulation 7(1): 68-81.
[8] Nguyen A-T, Reiter S, Rigo P (20149 A review on simulation-based optimization methods applied to building performance analysis. Applied Energy 113: 1043-1058.
[9] Costa-Carrapiço I, Raslan R, González JN (2020) A systematic review of genetic algorithm-based multi-objective optimisation for building retrofitting strategies towards energy efficiency. Energy and Buildings 210: 109690.
[10] Bucking S, Zmeureanu R, Athienitis A (2014) A methodology for identifying the influence of design variations on building energy performance. Journal of Building Performance Simulation 7(6): 411-426.
[11] Wilson A, Templeman A (1976) An approach to the optimum thermal design of office buildings. Building and Environment 11(1): 39-50.
[12] Bouchlaghem N, Letherman K (1990) Numerical optimization applied to the thermal design of buildings. Building and Environment 25(2): 117- 124.
[13] Chantrelle FP, Lahmidi H, Keilholz W, El Mankibi M, Michel P (2011) Development of a multicriteria tool for optimizing the renovation of buildings. Applied Energy 88(4): 1386-1394.
[14] Bambrook S, Sproul AB, Jacob D (2011) Design optimisation for a low energy home in Sydney. Energy and Buildings 43(7): 1702-1711.
[15] Hamdy M, Hasan A, Siren K (2011) Applying a multi-objective optimization approach for design of low-emission cost-effective dwellings. Building and environment 46(1): 109-123.
[16] Sahu M, Bhattacharjee B, Kaushik S (2012) Thermal design of air-conditioned building for tropical climate using admittance method and genetic algorithm. Energy and Buildings 53: 1-6.
[17] Gong X, Akashi Y, Sumiyoshi D (2012) Optimization of passive design measures for residential buildings in different Chinese areas. Building and Environment 58: 46-57.
[18] Hamdy M, Hasan A, Siren K (2013) A multi-stage optimization method for cost-optimal and nearlyzero-energy building solutions in line with the EPBD-recast 2010. Energy and Buildings 56: 189- 203.
[19] Kwong QJ, Adam NM, Sahari B (2014) Thermal comfort assessment and potential for energy efficiency enhancement in modern tropical buildings: A review. Energy and Buildings 68: 547- 557.
[20] Asadi E, da Silva MG, Antunes CH, Dias L, Glicksman L (2014) Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application. Energy and Buildings 81: 444-456.
[21] .Murray SN, Walsh BP, Kelliher D, O'Sullivan D (2014) Multi-variable optimization of thermal energy efficiency retrofitting of buildings using static modelling and genetic algorithms–A case study. Building and Environment 75: 98-107.
[22] Karaguzel OT, Zhang R, Lam KP, editors. Coupling of whole-building energy simulation and multi-dimensional numerical optimization for minimizing the life cycle costs of office buildings. Building Simulation; 2014: Springer.
[23] Ascione F, Bianco N, De Masi RF, Mauro GM, Vanoli GP (2017) Energy retrofit of educational buildings: Transient energy simulations, model calibration and multi-objective optimization towards nearly zero-energy performance. Energy and Buildings 144: 303-319.
[24] Kim J, Hong T, Jeong J, Koo C, Jeong K (2016) An optimization model for selecting the optimal green systems by considering the thermal comfort and energy consumption. Applied Energy 169: 682- 695.
[25] Fonseca Casas A, Ortiz J, Garrido N, Fonseca P, Salom J (2018) Simulation model to find the best comfort, energy and cost scenarios for building refurbishment. Journal of Building Performance Simulation 11(2): 205-222.
[26] Gagnon R, Gosselin L, Park S, Stratbücker S, Decker S (2019) Comparison between two genetic algorithms minimizing carbon footprint of energy and materials in a residential building. Journal of Building Performance Simulation 12(2): 224-242.
[27] Mahdavi A (2020) In the matter of simulation and buildings: Some critical reflections. Journal of Building Performance Simulation 13(1): 26-33.
[28] Magnier L, Haghighat F (2010) Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network. Building and Environment 45(3): 739-746.
[29] Yigit S, Ozorhon B (2018) A simulation-based optimization method for designing energy efficient buildings. Energy and Buildings 178: 216-227.
[30] EN U. Energy performance of buildings–economic evaluation procedure for energy systems in buildings. 2017.
[31] Pedersen CO, Fisher DE, Liesen RJ. Development of a heat balance procedure for calculating cooling loads. American Society of Heating, Refrigerating and Air-Conditioning Engineers, 1997. Report No.: 0001-2505.
[32] Goldberg DE, Korb B, Deb K (1989) Messy genetic algorithms: Motivation, analysis, and first results. Complex Systems 3(5): 493-530.
[33] AL‐Tabtabai H, Alex AP (1999) Using genetic algorithms to solve optimization problems in construction. Engineering Construction and Architectural Management 6(2): 121-132.
[34] Palonen M, Hasan A, Siren K. A genetic algorithm for optimization of building envelope and HVAC system parameters. Proc IBPSA’09. 2009:159-66.
[35] Turkish Statistical Institute, Building Occupancy Permit Data, Available at http://www.tuik.gov.tr. Accessed 3 Dec 2019.
[36] Comite'Europe'en de Normalisation C. Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics. EN 15251. 2007.
[37] DOE U. Commercial prototype building models. US Department of Energy (DOE). 2018.
[38] Fathi S, Srinivasan RS, Kibert CJ, Steiner RL, Demirezen E (2020) AI-based campus energy use prediction for assessing the effects of climate change. Sustainability 12(8): 3223.
[39] Ascione F, Bianco N, De Masi RF, Mauro GM, Vanoli GP (2015) Design of the building envelope: A novel multi-objective approach for the optimization of energy performance and thermal comfort. Sustainability 7(8): 10809-10836.
[40] Hamdy M, Sirén K (2016) A multi-aid optimization scheme for large-scale investigation of costoptimality and energy performance of buildings. Journal of Building Performance Simulation 9(4): 411-430.
[41] Longo S, Montana F, Sanseverino ER (2019) A review on optimization and cost-optimal methodologies in low-energy buildings design and environmental considerations. Sustainable Cities and Society 45: 87-104.
[42] Westermann P, Evins R (2019) Surrogate modelling for sustainable building design–a review. Energy and Buildings 198: 170-186.
[43] Østergård T, Jensen RL, Maagaard SE (2016) Building simulations supporting decision making in early design–A review. Renewable and Sustainable Energy Reviews 61: 187-201.
[44] Morrissey J, Horne RE (20119 Life cycle cost implications of energy efficiency measures in new residential buildings. Energy and Buildings 43(4): 915-924.
[45] Çağlayan S, Özorhon B, Özcan-Deniz G, Yiğit S (2020) A life cycle costing approach to determine the optimum insulation thickness of existing buildings. Isı Bilimi ve Tekniği Dergisi 40(1): 1-14.
[46] Aydin N, Biyikoğlu A (2021) Determination of optimum insulation thickness by life cycle cost analysis for residential buildings in Turkey. Science and Technology for the Built Environment 27(1): 2-13.
[47] Caglayan S, Yigit S, Ozorhon B, Ozcan-Deniz G, editors. A genetic algorithm-based envelope design optimisation for residential buildings. Proceedings of the Institution of Civil Engineers-Engineering Sustainability; 2020: Thomas Telford Ltd.
[48] Yigit S, Caglayan S, Ozorhon B (2019) Evaluation of optimum building envelope materials in different climate regions of Turkey. Materials Science and Engineering 471(6): 062009.
[49] Dombaycı ÖA, Gölcü M, Pancar Y (2006) Optimization of insulation thickness for external walls using different energy-sources. Applied Energy 83(9): 921-928.
[50] EmanueleNaboni AM, Korolija I, Zhang Y, editors. Comparison of conventional, parametric and evolutionary optimization approaches for the architectural design of nearly zero energy buildings. Proceedings of the 13th Conference of International Building Performance Simulation Association, Chambéry, France; 2013.
[51] Balijepalli VM, Khaparde S, editors. Effect of cost related parameters on optimization of Zero Net Energy Buildings. 2014 IEEE PES General Meeting| Conference & Exposition; 2014: IEEE.
[52] Negendahl K, Nielsen TR (2015) Building energy optimization in the early design stages: A simplified method. Energy and Buildings 105: 88- 99.
[53] Carlucci S, Cattarin G, Causone F, Pagliano L (2015) Multi-objective optimization of a nearly zero-energy building based on thermal and visual discomfort minimization using a non-dominated sorting genetic algorithm (NSGA-II). Energy and Buildings 104: 378-394.
[54] Schwartz Y, Raslan R, Mumovic D (2016) Implementing multi objective genetic algorithm for life cycle carbon footprint and life cycle cost minimisation: A building refurbishment case study. Energy 97: 58-68.
[55] Dhariwal J, Banerjee R, editors. An approach for building design optimization using design of experiments. Building Simulation; 2017: Springer.

APA	Yigit S, Ozorhon B (2021). Effectiveness of standalone simulation-based optimization software in optimizing the life cycle cost of residential buildings. , 210 - 228. 10.31462/jcemi.2021.04210228
Chicago	Yigit Sadik,Ozorhon Beliz Effectiveness of standalone simulation-based optimization software in optimizing the life cycle cost of residential buildings. (2021): 210 - 228. 10.31462/jcemi.2021.04210228
MLA	Yigit Sadik,Ozorhon Beliz Effectiveness of standalone simulation-based optimization software in optimizing the life cycle cost of residential buildings. , 2021, ss.210 - 228. 10.31462/jcemi.2021.04210228
AMA	Yigit S,Ozorhon B Effectiveness of standalone simulation-based optimization software in optimizing the life cycle cost of residential buildings. . 2021; 210 - 228. 10.31462/jcemi.2021.04210228
Vancouver	Yigit S,Ozorhon B Effectiveness of standalone simulation-based optimization software in optimizing the life cycle cost of residential buildings. . 2021; 210 - 228. 10.31462/jcemi.2021.04210228
IEEE	Yigit S,Ozorhon B "Effectiveness of standalone simulation-based optimization software in optimizing the life cycle cost of residential buildings." , ss.210 - 228, 2021. 10.31462/jcemi.2021.04210228
ISNAD	Yigit, Sadik - Ozorhon, Beliz. "Effectiveness of standalone simulation-based optimization software in optimizing the life cycle cost of residential buildings". (2021), 210-228. https://doi.org/10.31462/jcemi.2021.04210228

APA	Yigit S, Ozorhon B (2021). Effectiveness of standalone simulation-based optimization software in optimizing the life cycle cost of residential buildings. Journal of Construction Engineering, Management & Innovation (Online), 4(4), 210 - 228. 10.31462/jcemi.2021.04210228
Chicago	Yigit Sadik,Ozorhon Beliz Effectiveness of standalone simulation-based optimization software in optimizing the life cycle cost of residential buildings. Journal of Construction Engineering, Management & Innovation (Online) 4, no.4 (2021): 210 - 228. 10.31462/jcemi.2021.04210228
MLA	Yigit Sadik,Ozorhon Beliz Effectiveness of standalone simulation-based optimization software in optimizing the life cycle cost of residential buildings. Journal of Construction Engineering, Management & Innovation (Online), vol.4, no.4, 2021, ss.210 - 228. 10.31462/jcemi.2021.04210228
AMA	Yigit S,Ozorhon B Effectiveness of standalone simulation-based optimization software in optimizing the life cycle cost of residential buildings. Journal of Construction Engineering, Management & Innovation (Online). 2021; 4(4): 210 - 228. 10.31462/jcemi.2021.04210228
Vancouver	Yigit S,Ozorhon B Effectiveness of standalone simulation-based optimization software in optimizing the life cycle cost of residential buildings. Journal of Construction Engineering, Management & Innovation (Online). 2021; 4(4): 210 - 228. 10.31462/jcemi.2021.04210228
IEEE	Yigit S,Ozorhon B "Effectiveness of standalone simulation-based optimization software in optimizing the life cycle cost of residential buildings." Journal of Construction Engineering, Management & Innovation (Online), 4, ss.210 - 228, 2021. 10.31462/jcemi.2021.04210228
ISNAD	Yigit, Sadik - Ozorhon, Beliz. "Effectiveness of standalone simulation-based optimization software in optimizing the life cycle cost of residential buildings". Journal of Construction Engineering, Management & Innovation (Online) 4/4 (2021), 210-228. https://doi.org/10.31462/jcemi.2021.04210228