Parametric Building Performance Simulation in the Early Architectural Design Stage: Mid-Rise Apartment in Hot and Dry Climate

Document Type : Original Article

Authors

1 M.A. in Architecture and Energy, School of Architecture and Urban Studies, University of Art, Tehran, Iran

2 Associate Professor, Department of Architecture, School of Architecture and Urban Studies, University of Art, Tehran, Iran

Abstract

Ceaseless increase of energy demand in building sector has become a challenge for designers, which is often combines with some goals like indoor air quality, environmental impact, and building costs. To support designers, building performance simulation is a common technique in development-design stages, however, its implementation in early stages is limited, even though early decisions have higher impact upon final performance and costs of buildings. Architects have to design more energy-efficient buildings due to the requirements of energy efficiency regulations in various countries. There are several simulation tools, which may help designers during the design process, to estimate the thermal performance of the building under consideration. However, architects are reluctant to use these tools for several reasons: they are not user-friendly, need detailed information about the specifications of the building elements, which are not known at the initial phase of the architectural design; building simulation models are time-consuming and the interpretation of the simulation results is difficult for architects. In this paper, we present a method for energy efficiency optimization that can be applied in the initial architectural design process. This method will help architects to select the optimized floor plan regarding the functional, thermal and lighting parameters in the preliminary stage of building design. Here we implement sensitivity analysis and simulation-based optimization in order to optimize the thermal comfort, energy and daylight performance of residential buildings in Tehran. These objective functions were simulated using EnergyPlus and Radiance software programs for individual residential building configurations that were generated by parametric modeling techniques. Two thousand simulations for one hundred building floor plans were performed to create a comprehensive dataset covering full ranges of design parameters. The floor plans were created using an algorithm developed by Eugenio Rodrigues. The algorithm generates floor plans regarding the adjacency and dimensions of the rooms, location, and size of door and window, together with the entrance location. The main distinction of this study compared to the similar researches is including floor plan design as one of the parameters of optimization in the hot-dry climate of Tehran. A residential unit, which is situated on the middle floor of a mid-rise apartment, was selected as the base model. The present study considered building floor plan, building construction materials, glass type, insulation thickness, floor height, WWRs for kitchen, bedroom and living room and color of the floor finishing, walls, and ceiling as design variables to achieve the optimize Energy Use Intensity (EUI), useful daylight illumination, and occupants’ adaptive comfort. A simulation-optimization tool that couples a multi-objective genetic algorithm to a whole-building performance simulation engine was applied in order to find the optimal set of design variables, and finally, the results of the energy and daylight simulations were implemented into a set of regression and simple sensitive analysis equation to predict the most effective variable in each objective. Sensitivity analysis showed that the type of floor planning is most effective parameter for all objectives except that external wall material is an effective parameter for EUI, and occupant comfort and WWR are effective for daylight quality.

Keywords


حافظی، محمدرضا، زمردیان، زهرا سادات، و تحصیلدوست، محمد (1395). فرایند دستیابی به نمای دوپوسته دارای بهره‌وری مناسب انرژی، نمونه موردی یک ساختمان اداری در تهران. مطالعات معماری ایران، 10، 101-122.
-  ریاضی، جمشید (1389). ویژگیهای کارکردی دیوارهای خارجی ساختمانهای متعارف (قابلیتهای عملکردی، رفتاری، ساختاری).تهران: مرکز تحقیقات ساختمان و مسکن.
-  ریاضی، جمشید (1394). ویژگیهای کارکردی دیوارهای داخلی ساختمانهای متعارف (قابلیتهای عملکردی، رفتاری، ساختاری).تهران: مرکز تحقیقات ساختمان و مسکن.
-  ریاضی، جمشید، و ماجدی، محمد حسین (1388).ویژگیهای کارکردی در و پنجره ساختمانهای متعارف (قابلیتهای عملکردی، رفتاری، ساختاری).تهران: مرکز تحقیقات ساختمان و مسکن.
-  سخندان سرخابی، زهرا، و خان‌محمدی، محمد علی (1394). بهینه کردن کارکرد انرژی دیوارهای بدون بازشو در جبهه‌های آفتابگیر.هویت شهر،9(23)، 73-89.
-  فیاض، ریما (1392). سطح بهینه پنجره ساختمان‌های مسکونی در اردبیل و تهران.نامه معماری و شهرسازی، 5(10)، 105-119.
-  قاسم‌زاده، مسعود (1391). معیارهای ابعادی و ملاحضات طراحی فضاهای واحد مسکونی شهری.تهران: مرکز تحقیقات راه، مسکن و شهرسازی.
-  مرکز تحقیقات ساختمان و مسکن (1389). مبحث نوزدهم مقررات ملی ساختمان. تهران: مرکز تحقیقات ساختمان و مسکن.
-  مهدوی‌نژاد، محمدجواد، طاهباز، منصوره، و دولت‌آبادی، مهناز (1395). بهینه‌سازی تناسبات و نحوه استفاده از رف نور در معماری کلاس‌های آموزشی. هنرهای زیبا - معماری و شهرسازی،21(2)، 81-92.
-  یوسفی، ملیکا، مداحی، سیدمهدی، و سهیلی‌فرد، مهدی (1396). بهینه‌سازی جداره خارجی ساختمان در راستای افزایش آسایش حرارتی ساکنان با بهره‌گیری از الگوریتم ژنتیک. اولین همایش بین المللی عمران، معماری و شهر سبز پایدار.
 
-   Ahmed, S., Weber, M., Liwicki, M., Langenhan, C., Dengel, A., & Petzold, F. (2014). Automatic analysis and sketch-based retrieval of architectural floor plans. Pattern Recognition Letters,35, 91-100.
-   Ascione, F., Bianco, N., Stasio, C. D., Mauro, G. M., & Vanoli, G. P. (2017). Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach. Energy, 118, 999-1017.
-   Attia, S., Gratia, E., Herde, A. D., & Hensen, J. L. M. (2012). Simulation-based decision support tool for early stages of zero-energy building design. Energy and Buildings, 49, 2-15.
-   ASHRAE, FUNIP (2013).ASHRAE handbook: fundamentals (IP Edition).USA: Ashrae.
-   Azari, R., Garshasbi, S., Amini, P., Rashed-Ali, H., & Mohammadi, Y. (2016). Multi-objective optimization of building envelope design for life cycle environmental performance.Energy and Buildings,126, 524-34.
-   Baglivo, C., Maria Congedo, P., & Fazio, A. (2014). Multi-criteria optimization analysis of external walls according to ITACA protocol for zero energy buildings in the mediterranean climate.Building and environment,82, 467-80.
-   Bichiou, Y., & Krarti, M. (2011). Optimization of envelope and HVAC systems selection for residential buildings. Energy and Buildings,43, 3373-82.
-   Bournas, I., & Haav, L. (2016).Multi-objective Optimization of Fenestration Design in Residential spaces.Malmö: The Case of MKB Greenhouse, Sweden.
-   Caruso, G., & Kämpf, J. H. (2015). Building shape optimisation to reduce air-conditioning needs using constrained evolutionary algorithms.Solar Energy,118, 186-196.
-   Chen, X., Hongxing, Y., & Weilong, Z. (2017). Simulation-based approach to optimize passively designed buildings: A case study on a typical architectural form in hot and humid climates. Renewable and Sustainable Energy Reviews, 82, 1712-1725.
-   Dogan, T., Saratsis, E., & Reinhart, C. (2015). The optimization potential of floor-plan typologies in early design energy modeling. 14th Conference of International Building Performance Simulation Association,Hyderabad, India, Dec. 7-9, 2015.
-   Ercan, B., & Elias-Ozkan, S. T. (2015). Performance-based parametric design explorations: A method for generating appropriate building components. Design Studies, 38, 33-53.
-   Evins, R. (2013). A review of computational optimisation methods applied to sustainable building design. Renewable and Sustainable Energy Reviews, 22, 230-45.
-   Fan, Y., & Xiaohua, X. (2017). A multi-objective optimization model for energy-efficiency building envelope retrofitting plan with rooftop PV system installation and maintenance.Applied Energy, 189, 327-335.
-   Fang, Y. (2017). Optimization of Daylighting and Energy Performance Using Parametric Design, Simulation Modeling, and Genetic Algorithms.North Carolina State University.
-   Futrell, B. J., Ozelkan, E. C., & Brentrup, D. (2015). Bi-objective optimization of building enclosure design for thermal and lighting performance. Building and environment,92, 591-602.
-   Gossard, D., Lartigue, B., & Thellier, F. (2013). Multi-objective optimization of a building envelope for thermal performance using genetic algorithms and artificial neural network. Energy and Buildings, 67, 253-260.
-   Granadeiro, V., Duarte, J. P., Correia, J. R., & Lea, V. M. S. (2013). Building envelope shape design in early stages of the design process: Integrating architectural design systems and energy simulation. Automation in construction, 32, 196-209.
-   Hendron, R., & Engebrecht, C. (2010). Building America house simulation protocols, National Renewable Energy Laboratory Golden (EERE).Washington, DC: Golden, CO: National Renewable Energy Laboratory. 
-   Hester, J., Gregory, J., & Kirchain, R. (2017). Sequential early-design guidance for residential single-family buildings using a probabilistic metamodel of energy consumption.Energy and Buildings, 134, 202-11.
-   Hygh, J. S., DeCarolis, J. F., Hill, D. B., & Ranjithan, S. R. (2012). Multivariate regression as an energy assessment tool in early building design. Building and environment,57, 165-75.
-   Konis, K., Gamas, A., & Kensek, K. (2016). Passive performance and building form: An optimization framework for early-stage design support.Solar Energy, 125, 161-79.
-   Liu, S., Meng, X., & Tam, C. (2015). Building information modeling based building design optimization for sustainability. Energy and Buildings,105, 139-53.
-   Löhnert, G., Dalkowski, A., & Sutter, W. (2003). Integrated Design Process: a guideline for sustainable and solar-optimised building design. Berlín: IEA International Energy Agency.
-   Machairas, V., Tsangrassoulis, A., & Axarli, K. (2014). Algorithms for optimization of building design: A review. Renewable and Sustainable Energy Reviews, 31, 101-12.
-   Merrell, P., Schkufza, E., & Koltun, V. (2010). Computer-generated residential building layouts. ACM Transactions on Graphics (TOG),29(6), 181.
-   Merriam-Webster (2017). Merriam-Webster online dictionary.
-   Miles, J. C., Sisk, G. M., & Moore, C. J. (2001). The conceptual design of commercial buildings using a genetic algorithm. Computers & Structures,79, 1583-92.
-   Negendahl, K., & Nielsen, T. R. (2015). Building energy optimization in the early design stages: A simplified method. Energy and Buildings,105, 88-99.
-   Rodrigues, E., Gaspar, A. R., & Gomes, Á. (2014). Improving thermal performance of automatically generated floor plans using a geometric variable sequential optimization procedure. Applied Energy, 132, 200-215.
-   Samuelson, H., Claussnitzer, S., Goyal, A., Chen, Y., & Romo-Castillo, A. (2016). Parametric energy simulation in early design: High-rise residential buildings in urban contexts. Building and environment, 101, 19-31.
-   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.
-   Tuhus-Dubrow, D., & Krarti, M. (2010). Genetic-algorithm based approach to optimize building envelope design for residential buildings. Building and environment,45, 1574-81.
-   Wang, W., Rivard, H., and Zmeureanu, R. (2005). An object-oriented framework for simulation-based green building design optimization with genetic algorithms. Advanced Engineering Informatics,19, 5-23.
-   Wang, W., Rivard, H., & Zmeureanu, R. (2006). Floor shape optimization for green building design. Advanced Engineering Informatics, 20(4), 363-378.
-   Wright, Jonathan A (1986). The optimised design of HVAC systems.Doctoral dissertation, Loughborough University, JA Wright.
-   Youssef, A., Ali, M. Z., Zhiqiang, J., & Reffat, R. M. (2016). Genetic algorithm based optimization for photovoltaics integrated building envelope. Energy and Buildings,127, 627-36.
-   Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm. TIK-report,103.
-   Zomorodian, Z. S., & Tahsildoost, M. (2017). Assessment of window performance in classrooms by long term spatial comfort metrics. Energy and Buildings,134, 80-93.
-   http://saba.org.ir/saba_content/media/image/2012/07/4118_orig.pdf
-   http://www.jaloxa.eu/resources/radiance/colour_picker/index.shtml