مرور پارامترهای طراحی پوسته ساختمان در جهت کاهش مصرف انرژی (نمونه موردی: بنا‌های مسکونی متداول منطقه 15)
محورهای موضوعی : مطالعات محله در شهرهای ایرانی اسلامیرضا سلیمی گرگری 1 , سید مجید مفیدی شمیرانی 2 , هانیه صنایعیان 3 *
1 - دانشجوی دکتری معماری، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران
2 - استادیار گروه معماری و شهرسازی، دانشگاه علم و صنعت، تهران
3 - استادیار گروه معماری و شهرسازی، دانشگاه علم و صنعت، تهران
کلید واژه: پوسته ساختمان, تیپولوژی, بار گرمایشی, مصرف انرژی, نمای پایدار.,
چکیده مقاله :
با توجه به نقش کلیدی نمای ساختمان به عنوان پوسته و تأثیرات آن بر کیفیت فضاهای داخلی و مصرف انرژی، بهینه سازی نما در فرآیند طراحی یک ساختمان بسیار حائز اهمیت است. از سوی دیگر با توجه به چالش‌ها و پیچیدگی روش‌های سنتی بهینه‌سازی، استفاده از روش‌های نوین برای ارزیابی در مراحل ابتدایی طراحی ضروری به نظر می‌رسد. شناسایی راهکارها و استراتژیهای بهینه سازی پارامترهای طراحی پوسته ساختمان، به معماران این امکان را می‌دهد که در همان مراحل اولیه طراحی تأثیر به سزایی در رفتار حرارتی ساختمان داشته باشند. مقاله حاضر یک مرور جامع با تأکید بر مطالعات انجام شده در سال‌های اخیر در زمینه پوسته ساختمان و پارامترهای مؤثر بر رفتار حرارتی داخل بناست و بخشی از تحقیقات گسترده‌تری است که هدف آن ارائه راهکارهای طراحی برای کاهش مصرف انرژی در نماها می‌باشد. هدف اصلی این تحقیق مطالعه مروری بر تمام منابع موجود در این زمینه می‌باشد و در این راستا، پارامترهای کالبدی نما بر اساس مطالعات انجام شده، مورد بررسی سیستماتیک قرار گرفته است و پس بررسی و مرورو دقیق مطالعات انجام شده در این زمینه، پارامترهای تأثیر‌گذار در پوسته ساختمان بر رفتار حرارتی داخلی بنا، استخراج و دسته‌بندی شده‌اند. در مرحله اول، با بررسی منابع مرتبط و مطالعات مشابه، پارامترهای کالبدی نما به صورت کامل بررسی شده است و در بخش دوم تیپ‌های مختلف نماها در منطقه 15 مورد بررسی قرار گرفته است.. نقشه GIS منطقه با دقت بررسی شده و تیپ‌های مختلف نماها به روش میدانی استخراج شده‌اند. سپس با در نظر گرفتن طول نماهای یکسان، بر اساس نقشه و فضاهای موجود در نماهای اصلی، بناها دسته‌بندی شده و گونه‌های نهایی مشخص می‌شوند.
The quality of the internal spaces and the impacts it has on the building's energy consumption depend critically on the right and optimal design of the building facade, which plays a significant function as the structure's envelope.Taking into account the facade's crucial function as the building's outer shell on On the other hand, new evaluation methods that designers and architects can utilize in the early stages of design must be replaced due to the time-consuming and challenging existing methods of optimization. The building's exterior envelope has an impact on both the outside environment and the urban environment in addition to shielding the inside environment from outside environmental conditions. Walls, ceilings, windows, doors, and other building elements are among its many parts. The goal of the current study is to create a thorough taxonomy of local structures while also examining the impact of building facade factors on their thermal behavior and energy usage. For this reason, the anatomical parameters of the façade are thoroughly studied in the first step by methodically studying sources and comparable research, and then the various types of facades in area 15 are examined. It should be noted that this article is only the first of a thorough investigation into facade design alternatives for energy consumption reduction. Following the completion of the investigations, the area's GIS map was extensively analyzed, and various and typical types were extracted using a field approach. According to the results of the field research and the components of the facade covering, the average area, orientation, length-to-width ratio, and frequency of parts are examined. The final types are extracted in the second stage after the buildings with the same length of view are classified in accordance with the map and the locations of the spaces in the main view.
Al-Homoud, M. S. (2005) A Systematic Approach for the Thermal Design Optimization of Building Envelopes. Journal of Building Physics, 29(2), 95-119. doi:10.1177/1744259105056267.
Anastaselos, D., Oxizidis, S., & Papadopoulos, A. M. (2011) Energy, environmental and economic optimization of thermal insulation solutions by means of an integrated decision support system. Energy and Buildings, 43(2), 686-694. doi:https://doi.org/10.1016/j.enbuild.2010.11.013.
Asadi, E., Silva, M. G. D., Antunes, C. H., 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. doi:10.1016/j.enbuild.2014.06.009.
Bojić, M., Miletić, M., & Bojić, L. (2014) Optimization of thermal insulation to achieve energy savings in low energy house (refurbishment). Energy Conversion and Management, 84, 681-690. doi:https://doi.org/10.1016/j.enconman.2014.04.095.
Bolattürk, A. (2006) Determination of optimum insulation thickness for building walls with respect to various fuels and climate zones in Turkey. Applied Thermal Engineering, 26(11), 1301-1309. doi:https://doi.org/10.1016/j.applthermaleng.2005.10.019.
Caldas, L. G., & Norford, L. K. (2002) A design optimization tool based on a genetic algorithm. Automation in Construction, 11(2), 173-184. doi:https://doi.org/10.1016/S0926-5805(00)00096-0.
Çomaklı, K., & Yüksel, B. (2004) Environmental impact of thermal insulation thickness in buildings. Applied Thermal Engineering, 24(5), 933-940. doi:https://doi.org/10.1016/j.applthermaleng.2003.10.020.
Cronshaw, I. (2015) World Energy Outlook 2014 projections to 2040: natural gas and coal trade, and the role of China. Australian Journal of Agricultural and Resource Economics, 59(4), 571-585. doi:https://doi.org/10.1111/1467-8489.12120.
Cvetković, D., & Bojić, M. (2014) Optimization of thermal insulation of a house heated by using radiant panels. Energy and Buildings, 85, 329-336. doi:https://doi.org/10.1016/j.enbuild.2014.09.043.
Day, J. K., & Gunderson, D. E. (2015) Understanding high performance buildings: The link between occupant knowledge of passive design systems, corresponding behaviors, occupant comfort and environmental satisfaction. Building and Environment, 84, 114-124. doi:https://doi.org/10.1016/j.buildenv.2014.11.003.
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. doi:https://doi.org/10.1016/j.buildenv.2015.03.039.
Gero, J. S., D'Cruz, N., & Radford, A. D. (1983) Energy in context: A multicriteria model for building design. Building and Environment, 18(3), 99-107. doi:https://doi.org/10.1016/0360-1323(83)90001-X.
González-Torres, M., Pérez-Lombard, L., Coronel, J. F., Maestre, I. R., & Yan, D. (2022) A review on buildings energy information: Trends, end-uses, fuels and drivers. Energy Reports, 8, 626-637. doi:https://doi.org/10.1016/j.egyr.2021.11.280.
Gou, S., Nik, V. M., Scartezzini, J.-L., Zhao, Q., & Li, Z. (2018) Passive design optimization of newly-built residential buildings in Shanghai for improving indoor thermal comfort while reducing building energy demand. Energy and Buildings, 169, 484-506. doi:1/0.1016j.enbuild.2017.09.095.
Hafez, F. S., Sa'di, B., Safa-Gamal, M., Taufiq-Yap, Y. H., Alrifaey, M., Seyedmahmoudian, M.,. Mekhilef, S. (2023) Energy Efficiency in Sustainable Buildings: A Systematic Review with Taxonomy, Challenges, Motivations, Methodological Aspects, Recommendations, and Pathways for Future Research. Energy Strategy Reviews, 45, 101013. doi:https://doi.org/10.1016/j.esr.2022.101013.
Hasan, A. (1999) Optimizing insulation thickness for buildings using life cycle cost. Applied Energy, 63(2), 115-124. doi:https://doi.org/10.1016/S0306-2619(99) 00023-9.
Heiselberg, P., Brohus, H., Hesselholt, A., Rasmussen, H., Seinre, E., & Thomas, S. (2009) Application of sensitivity analysis in design of sustainable buildings. Renewable Energy, 3. 2030-2036, (9), doi:https://doi.org/10.1016/j.renene.2009.02.016.
Huang, J., Lv, H., Gao, T., Feng, W., Chen, Y., & Zhou, T. (2014) Thermal properties optimization of envelope in energy-saving renovation of existing public buildings. Energy and Buildings, 75, 504-510. doi:https://doi.org/10.1016/j.enbuild.2014.02.040.
Ioannou, A., & Itard, L. (2015) Energy Performance and comfort in residential buildings: Sensitivity for building parameters and occupancy. Energy and Buildings, 92. doi:10.1016/j.enbuild.2015.01.055.
Iwaro, J., & Mwasha, A. (2014) The Impact of Sustainable Building Envelope Design on Building Sustainability Using Integrated Performance Model. International Journal of Sustainable Built Environment, 2. doi:10.1016/j.ijsbe.2014.03.002.
Iwaro, J., Mwasha, A., Williams, R., & Wilson, W. (2014) The role of integrated performance model in sustainable envelope design and assessment. International Journal of Sustainable Engineering, 8, 1-23. doi:10.1080/19397038.2014.930211.
Jiang, F., Wang, X., & Zhang, Y. (2012) Analytical optimization of specific heat of building internal envelope. Energy Conversion and Management, 63, 239-244. doi:https://doi.org/10.1016/j.enconman.2012.01.038.
Kaynakli, O. (2012). A review of the economical and optimum thermal insulation thickness for building applications. Renewable and Sustainable Energy Reviews, 16(1), 415-425. doi:https://doi.org/10.1016/j.rser.2011.08.006.
Kumar, G., & Raheja, G. (2016) Design Determinants of Building Envelope for Sustainable Built Environment: A Review.
Lee, J. W., Jung, H. J., Park, J. Y., Lee, J. B., & Yoon, Y. (2013) Optimization of building window system in Asian regions by analyzing solar heat gain and daylighting elements. Renewable Energy, 50, 522-531. doi:https://doi.org/10.1016/j.renene.2012.07.029.
Li, H., & Wang, S. (2020) Coordinated robust optimal design of building envelope and energy systems for zero/low energy buildings considering uncertainties. Applied Energy, 265, 114779. doi:https://doi.org/10.1016/j.apenergy.2020.114779.
Lollini, R., Barozzi, B., Fasano, G., Meroni, I., & Zinzi, M. (2006) Optimisation of opaque components of the building envelope. Energy, economic and environmental issues. Building and Environment, 1001-1013.
Lulic, H., Civic, A., Pasic, M., Omerspahic, A., & Dzaferovic, E. (2014) Optimization of Thermal Insulation and Regression Analysis of Fuel Consumption. Procedia Engineering, 69, 902-910. doi:https://doi.org/10.1016/j.proeng.2014.03.069.
Luo, M., Arens, E., Zhang, H., Ghahramani, A., & Wang, Z (2018)) Thermal comfort evaluated for combinations of energy-efficient personal heating and cooling devices. Building and Environment, 143, 206-216. doi:https://doi.org/10.1016/j.buildenv.2018.07.008.
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. doi:10.1016/j.buildenv.2009.08.016.
Monsen, W. A., Klein, S. A., & Beckman, W. A. (1981) Prediction of direct gain solar heating sytem performance. Solar Energy, 27(2), 143-147. doi:https://doi.org/10.1016/0038-092X(81)90036-0.
Moon, J. W., & Jung, S. K. (2016) Development of a thermal control algorithm using artificial neural network models for improved thermal comfort and energy efficiency in accommodation buildings. Applied Thermal Engineering, 103, 1135-1144. doi:https://doi.org/10.1016/j.applthermaleng.2016.05.002.
Mostavi, E., Asadi, S., & Boussaa, D. (2017) Development of a new methodology to optimize building life cycle cost, environmental impacts, and occupant satisfaction. Energy, 121, 606-615. doi:https://doi.org/10.1016/j.energy.2017.01.049.
Nematchoua, M. K., Tchinda, R., & Orosa, J. A. (2014) Thermal comfort and energy consumption in modern versus traditional buildings in Cameroon: A questionnaire-based statistical study. Applied Energy, 114, 687-699.
Nyers, J., Kajtar, L., Tomić, S., & Nyers, A. (2015) Investment-savings method for energy-economic optimization of external wall thermal insulation thickness. Energy and Buildings, 86, 268-274. doi:https://doi.org/10.1016/j.enbuild.2014.10.023.
Oliveira, A. C., & de Oliveira Fernandes, E. (1992) A new simplified method for evaluating the thermal behaviour of direct gain passive solar buildings. Solar Energy, 48(4), 227-233. doi:https://doi.org/10.1016/0038-092X(92)90095-R.
Ouarghi, R., & Krarti, M. (2006) Building shape optimization using neural network and genetic algorithm approach, Chicago, IL.
Ozel, M. (2011) Thermal performance and optimum insulation thickness of building walls with different structure materials. Applied Thermal Engineering, 31(17), 3854-3863. doi:https://doi.org/10.1016/j.applthermaleng.2011.07.033.
Ozel, M. (2014) Effect of insulation location on dynamic heat-transfer characteristics of building external walls and optimization of insulation thickness. Energy and Buildings, 72, 288-295. doi:https://doi.org/10.1016/j.enbuild.2013.11.015.
Pérez-Lombard, L., Ortiz, J., & Velázquez, D. (2013) Revisiting energy efficiency fundamentals. Energy Efficiency, 6(2), 239-254. doi:10.1007/s12053-012-9180-8.
Rakha, T., & Nassar, K. (2011). Genetic algorithms for ceiling form optimization in response to daylight levels. Renewable Energy, 36(9), 2348-2356. doi:https://doi.org/10.1016/j.renene.2011.02.006.
Roberts, B. C., Webber, M. E., & Ezekoye, O. A. (2015) Development of a multi-objective optimization tool for selecting thermal insulation materials in sustainable designs. Energy and Buildings, 105, 358-367. doi:https://doi.org/10.1016/j.enbuild.2015.07.063.
Samarasinghalage, T. I., Wijeratne, W. M. P. U., Yang, R. J., & Wakefield, R. (2022) A multi-objective optimization framework for building-integrated PV envelope design balancing energy and cost. Journal of Cleaner Production, 342, 13.0930 doi:10.1016/j.jclepro.2022.130930.
Stavrakakis, G. M., Zervas, P. L., Sarimveis, H., & Markatos, N. C. (2012). Optimization of window-openings design for thermal comfort in naturally ventilated buildings. Applied Mathematical Modelling, 36(1), 193-211. doi:https://doi.org/10.1016/j.apm.2011.05.052.
Tian, Z., Shi, X., & Hong, S.-M. (2021). Exploring data-driven building energy-efficient design of envelopes based on their quantified impacts. Journal of Building Engineering, 42, 103018. doi:https://doi.org/10.1016/j.jobe.2021.103018.
Tuhus-Dubrow, D., & Krarti, M. (2010) Genetic-algorithm based approach to optimize building envelope design for residential buildings. Building and Environment, 45(7), 1574-1581. doi:https://doi.org/10.1016/j.buildenv.2010.01.005.
Vera, S., Uribe, D., Bustamante, W., & Molina, G. (2017) Optimization of a fixed exterior complex fenestration system considering visual comfort and energy performance criteria. Building and Environment, 113, 163-174. doi:https://doi.org/10.1016/j.buildenv.2016.07.027.
Wang, L., Wong Nyuk, H., & Li, S. (2007) Facade design optimization for naturally ventilated residential buildings in Singapore. Energy and Buildings, 39(8), 954-961. doi:https://doi.org/10.1016/j.enbuild.2006.10.011.
Whillier, A. (19).53. Solar energy collection and its utilization for house heating.
Xu, S., Yu, Z., Yang, C., Ji, X., & Zhang, K. (2018) Trends in evapotranspiration and their responses to climate change and vegetation greening over the upper reaches of the Yellow River Basin. Agricultural and Forest Meteorology, 263, 118-129. doi:https://doi.org/10.1016/j.agrformet.2018.08.010.
Yu, W., Li, B., Jia, H., Zhang, M., & Wang, D. (2015) Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design. Energy and Buildings, 88, 135-143. doi:https://doi.org/10.1016/j.enbuild.2014.11.063.
Zahiri, S., & Elsharkawy, H. (2018) Towards energy-efficient retrofit of council housing in London: Assessing the impact of occupancy and energy-use patterns on building performance. Energy and Buildings, 174, 672-681. doi:https://doi.org/10.1016/j.enbuild.2018.07.010.
Zemella, G., De March, D., Borrotti, M., & Poli, I. (2011) Optimised design of energy efficient building façades via Evolutionary Neural Networks. Energy and Buildings, 43(12), 3297-3302. doi:https://doi.org/10.1016/j.enbuild.2011.10.006.