تحقیقات کاربردی علوم جغرافیایی

تحقیقات کاربردی علوم جغرافیایی

بهینه سازی هزینه ساخت و مصرف انرژی در طول دوره بهره برداری ساختمانهای مسکونی (نمونه موردی: ساختمانهای مسکونی متداول در تهران)

نویسندگان
میثم ذکاوت 1
منصوره طاهباز 2
محمدرضا حافظی 3
1 دانشجوی دکتری معماری دانشکده معماری و شهرسازی، دانشگاه شهید بهشتی، تهران، ایران
2 دانشیار دانشکده معماری و شهرسازی، دانشگاه شهید بهشتی، تهران ایران
3 دانشیار دانشکده معماری و شهرسازی، دانشگاه شهید بهشتی، تهران، ایران
10.61186/jgs.23.70.425
چکیده
ساختمانها یکی از ارکان اصلی توسعه اقتصادی، اجتماعی کشورها هستند که بخش زیادی از انرژی و منابع طبیعی را مصرف میکنند. هدف از نگارش این مقاله، محاسبه میزان انرژی مصرفی یک ساختمان در یکسال ، تعیین برچسب انرژی و سپس بهینه سازی برخی از ویژگیهای آن با هدف کاهش هزینه ساخت و کاهش مصرف انرژی فاز بهره برداریست. است. محدوده مورد مطالعه، ساختمانهای مسکونی متداول در شهر تهران میباشد. روش تحقیق برای رسیدن به این هدف بدین گونه میباشد: ابتدا با توجه به آمار صدور پروانه شهرداری تهران، یک ساختمان جنوبی 5 طبقه، در زمینی به مساحت 320متر مربع و با زیربنای حدود 1100 متر مربع انتخاب گردید که نماینده تعداد زیادی از ساختمانهای شهر تهران باشد. پس از آن با شبیه سازی ساختمان در نظر گرفته شده در نرم افزار دیزاین بیلدر، میزان گاز مصرفی، 53/145 کیلووات ساعت بر متر مربع، برق مصرفی، 25/81 کیلووات ساعت بر متر مربع و مجموعاً 79/226 کیلووات ساعت بر متر مربع در سال محاسبه گردید. با این میزان مصرف، با توجه به استاندارد 14253 ، ساختمان برچسب انرژی C دریافت میکند. سپس با هدف کاهش هزینه ساخت و مصرف انرژی دوران بهره برداری، اقدام به بهینه سازی با الگوریتم ژنتیک گردید. متغیرهای تحقیق، جنس دیوارخارجی(سفال یا لیکا)، نمای خارجی(سنگ یا آجر)، نوع شیشه پنجره(ساده یا کم گسیل)، نوع گاز بین لایه های شیشه پنجره(هوا یا ارگون) و نسبت پنجره به سطح جبهه جنوبی (15%-30%-45%-60%) بنا بودند. نتایج تحقیق حاکی از این است که بهینه ترین حالات ممکن برای نمای خارجی، سنگ، شیشه ها دوجداره کم گسیل همراه با گاز آرگون، جنس دیوار خارجی سفال با نسبت پنجره به سطح جبهه جنوبی، 5/22 % و یا لیکا، با نسبت پنجره به سطح جبهه جنوبی، 5/37 % میباشد.
کلیدواژه‌ها
بهینه سازی
هزینه ساخت
مصرف انرژی
شبیه سازی
دیزاین بیلدر
الگوریتم ژنتیک

عنوان مقاله English

Optimization of construction costs and energy consumption during the operation period of residential buildings (Case study: common residential buildings in Tehran)

نویسندگان English

Meysam Zekavat 1
Mansoureh Tahabaz 2
Mohammad Reza Hafezi 3
1 PhD Student in Architecture, Department of Architecture, Faculty of Architecture and Urban Planning, Shahid Beheshti University, Tehran, Iran
2 Associate Professor, Department of Architecture, Faculty of Architecture and Urban Planning, Shahid Beheshti University, Tehran, Iran
3 Associate Professor, Department of Architecture, Faculty of Architecture and Urban Planning, Shahid Beheshti University, Tehran, Iran
چکیده English

Buildings are one of the main pillars of economic and social development of countries that consume a large part of energy and natural resources. The purpose of writing this article is to calculate the energy consumption of a building in one year, determine the energy label and then optimize some of its features in order to reduce construction costs and reduce energy consumption of the operation phase. Is. The study area is common residential buildings in Tehran. The research method to achieve this goal is as follows: First, according to the licensing statistics of Tehran Municipality, a 5-storey southern building was selected on a land with an area of ​​320 square meters and an infrastructure of about 1100 square meters, which represents a large number of buildings in Tehran. Be. Then, by simulating the building considered in Builder Design software, the amount of gas consumption is 145.53 kWh per square meter, electricity consumption is 81.25 kWh per square meter and a total of 226.79 kWh per meter. The square was calculated per year. With this consumption, according to standard 14253, the building receives energy label C. Then, with the aim of reducing the cost of construction and energy consumption during operation, it was optimized with a genetic algorithm. Research variables, type of exterior wall (pottery or Leica), exterior (stone or brick), type of window glass (plain or low emission), type of gas between the layers of window glass (air or argon) and the ratio of the window to the south front surface (15 % -30% -45% -60%) were built. The results indicate that the best possible scenarios for the exterior facade, stone, low-emission double-glazed windows with argon gas, the outer wall of the pottery with a window-to-south front ratio of 22.5% or Leica, with a window ratio At the level of the southern front, it is 37.5%.

کلیدواژه‌ها English

Optimization
Construction Cost
Energy Consumption
Simulation
Builder Design
Genetic Algorithm
1. Alaidroos, A., & Krarti, M. (2015). Optimal design of residential building envelope systems in the Kingdom of Saudi Arabia. Energy and Buildings, 86, 104-117.‌
2. Aria, H., & Akbari, H. (2014). Integrated and multi-hour optimization of office building energy consumption and expenditure. Energy and buildings, 82, 391-398.‌
3. Asadi, E., Da Silva, M. G., Antunes, C. H., & Dias, L. (2012). Multi-objective optimization for building retrofit strategies: A model and an application. Energy and Buildings, 44, 81-87.‌
4. Attia, S., Hamdy, M., O’Brien, W., & Carlucci, S. (2013). Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design. Energy and Buildings, 60, 110-124.‌
5. Bambrook, S. M., Sproul, A. B., & Jacob, D. (2011). Design optimisation for a low energy home in Sydney. Energy and Buildings, 43(7), 1702-1711.‌
6. Caldas, L. (2008). Generation of energy-efficient architecture solutions applying GENE_ARCH: An evolution-based generative design system. Advanced Engineering Informatics, 22(1), 59-70.‌
7. Caldas, L. (2011, March). Generation of energy-efficient patio houses: combining GENE_ARCH and a Marrakesh Medina shape grammar. In 2011 AAAI Spring Symposium Series.‌
8. Caldas, L. G., & Norford, L. K. (2003). Genetic algorithms for optimization of building envelopes and the design and control of HVAC systems. J. Sol. Energy Eng., 125(3), 343-351.‌
9. Carreras, J., Boer, D., Guillén-Gosálbez, G., Cabeza, L. F., Medrano, M., & Jiménez, L. (2015). Multi-objective optimization of thermal modelled cubicles considering the total cost and life cycle environmental impact. Energy and Buildings, 88, 335-346.‌
10. Chantrelle, F. P., 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.‌
11. D'cruz, N., Radford, D., Gero, S. (1983). A Pareto optimization problem formulation for building performance and design. Engineering optimization, 7(1), 17-33.‌
12. 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.‌
13. Flager, F., Welle, B., Bansal, P., Soremekun, G., & Haymaker, J. (2009). Multidisciplinary process integration and design optimization of a classroom building. Journal of Information Technology in Construction (ITcon), 14(38), 595-612.‌
14. Hamdy, M., Nguyen, A. T., & Hensen, J. L. (2016). A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems. Energy and Buildings, 121, 57-71.‌
15. Han, X., Pei, J., Liu, J., & Xu, L. (2013). Multi-objective building energy consumption prediction and optimization for eco-community planning. Energy and buildings, 66, 22-32.‌
16. Karaguzel, O. T., Zhang, R., & Lam, K. P. (2014, April). Coupling of whole-building energy simulation and multi-dimensional numerical optimization for minimizing the life cycle costs of office buildings. In Building simulation (Vol. 7, No. 2, pp. 111-121). Tsinghua University Press.‌
17. Karatas, A., & El-Rayes, K. (2015). Optimizing tradeoffs among housing sustainability objectives. Automation in construction, 53, 83-94.‌
18. Kusiak, A., Xu, G., & Tang, F. (2011). Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm. Energy, 36(10), 5935-5943.‌
19. Lee, B., & Hensen, J. L. (2013). Towards zero energy industrial halls–simulation and optimization with integrated design approach. In Proceedings of the thirteenth international IBPSA conference.‌
20. 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.‌
21. Naboni, E., Maccarini, A., Korolija, I., & Zhang, Y. (2013, August). Comparison of conventional, parametric and evolutionary optimization approaches for the architectural design of nearly zero energy buildings. In Building Simulation 2013 (pp. 2558-2565).‌
22. Penna, P., Prada, A., Cappelletti, F., & Gasparella, A. (2015). Multi-objectives optimization of Energy Efficiency Measures in existing buildings. Energy and Buildings, 95, 57-69.‌
23. Ramin, H., Hanafizadeh, P., Ehterami, T., & AkhavanBehabadi, M. A. (2019). Life cycle-based multi-objective optimization of wall structures in climate of Tehran. Advances in Building Energy Research, 13(1), 18-31.‌
24. Rapone, G., & Saro, O. (2012). Optimisation of curtain wall façades for office buildings by means of PSO algorithm. Energy and Buildings, 45, 189-196.‌
25. Sajadi,B., Baniassadi, A. (2015). On the effect of using phase change materials in building energy consumption and CO2 emission in Iran: climatic and parametric study, Energy Equipment andSystems, 3(2): 73-81.
26. Salminen, M., Palonen, M., & Sirén, K. (2012, September). Combined energy simulation and multi-criteria optimisation of a LEED-certified building. In Proceedings of the building simulation and optimization conference (pp. 372-377). Loughborough University Loughborough.‌
27. Salminen, M., Palonen, M., & Sirén, K. (2012, September). Combined energy simulation and multi-criteria optimisation of a LEED-certified building. In Proceedings of the building simulation and optimization conference (pp. 372-377). Loughborough University Loughborough.‌
28. Shi, X. (2011). Design optimization of insulation usage and space conditioning load using energy simulation and genetic algorithm. Energy, 36(3), 1659-1667.‌
29. Shi, X., Tian, Z., Chen, W., Si, B., & Jin, X. (2016). A review on building energy efficient design optimization rom the perspective of architects. Renewable and Sustainable Energy Reviews, 65, 872-884.‌
30. Taheri, M., Tahmasebi, F., & Mahdavi, A. (2012). A case study of optimization-aided thermal building performance simulation calibration. Optimization, 4(2nd).‌
31. Touloupaki, E., & Theodosiou, T. (2017). Performance simulation integrated in parametric 3D modeling as a method for early stage design optimization—A review. Energies, 10(5), 637.‌
32. Tresidder, E., Zhang, Y., & Forrester, A. I. (2012). Acceleration of building design optimisation through the use of kriging surrogate models. Proceedings of building simulation and optimization, 1-8.‌
33. Wang, W., Rivard, H., & Zmeureanu, R. (2006). Floor shape optimization for green building design. Advanced Engineering Informatics, 20(4), 363-378.‌
34. Wu, M. H., Ng, T. S., & Skitmore, M. R. (2016). Sustainable building envelope design by considering energy cost and occupant satisfaction. Energy for sustainable development, 31, 118-129.‌
35. Znouda, E., Ghrab-Morcos, N., & Hadj-Alouane, A. (2007). Optimization of Mediterranean building design using genetic algorithms. Energy and buildings, 39(2), 148-153.‌