ارزيابي و تدوين شاخص‌هاي تجويزي مصرف انرژي در ساختمان¬هاي اداري
محورهای موضوعی : مهندسی برق- قدرت
مهرداد محمودیان
1
,
سجاد سعدی
2
1 - موسسه آموزش عالی آپادانا، شیراز، ايران
2 - مربی/دانشگاه امام حسین
کلید واژه: شاخص, پايداري انرژي, بهينه سازي مصرف انرژي, دسته بندي مصرف انرژي,
چکیده مقاله :
بهبود مصرف انرژي و کاهش آلودگي محيط زيست از محورهاي اساسي پايداري انرژي است. حوزه ساختمان به عنوان يکي از بخش‌هاي اصلي مصرف انرژي، بيش از 40 درصد از مصرف انرژي کل را دارد. در اين مقاله، با بررسي بخش‌هاي مصرف کننده انرژي در¬ساختمان با استفاده از روش تحليل سلسله مراتبي به منظور برچسب دهي و بهينه سازي مصرف انرژي ساختمان‌هاي اداري، شاخص‌هاي تاثيرگذار شناسايي و معرفي شده‌اند. در اين پژوهش ساختمان‌هاي اداري به کمک مطالعات پيشين به بخش‌هاي مختلف تقسيم‌بندي شده است و سپس در هر بخش شاخص‌هاي مربوطه از مرور ادبيات موضوع استخراج شده است. تعداد 62 شاخص در 12 بخش مختلف استخراج شد که پس از بررسي آماري به روش‌هاي آلفاي کرونباخ، CVR و CVI تعداد 47 شاخص مورد تاييد قرار گرفتند. به اين ترتيب که ۴7 شاخص تاييد شده اثرگذاري زيادي بر مصرف انرژي ساختمان دارند و شاخص‌هاي رد شده اثرگذاري کمتري نسبت به آنها دارند.
Most of the energy consumed in the building is consumed during the operation of the building. If you save on this part of the building energy, you can have a great impact on energy consumption. One way to control energy consumption in buildings is to use building energy labels. In order to provide an energy label, it must be possible to identify the factors affecting the energy consumption of the building. Introducing and studying energy-related indicators is one of the appropriate methods for managing energy consumption. With the help of these indicators, energy consumption can be managed and optimized. In this paper, by examining the main sectors of energy consumption in the buildings using the hierarchical analysis method (in order to label and optimize the energy consumption of office buildings), effective Attributes have been identified and introduced. The systematic search focuses on a variety of sources, including documents, databases, conferences, and various journals. First, search terms are selected. After the findings of library studies, field studies are conducted. This data can be described as experimental or new data. In the present study, two questionnaires were used to conduct field studies and obtain the required information. Improving energy consumption and reducing environmental pollution are key to sustainable energy. As the major consumer of energy, the building sector accounts for more than 40% of total energy consumption. One of the most important consumers of energy are office buildings, which include applications such as air conditioning, heating, cooling, hot water and various equipment. In this research, office buildings have been divided into different sections with the help of previous studies, and then in each section, the relevant Attributes have been extracted from the literature review. A total of 62 indices were extracted in 12 different sections. .
[1] Doukas, H., C. Nychtis, and J. Psarras, Assessing energy-saving measures in buildings through an intelligent decision support model. Building and environment, 2009. 44(2): p. 290-298.
[2] Harish, V. and A. Kumar, A review on modeling and simulation of building energy systems. Renewable and sustainable energy reviews, 2016. 56: p. 1272-1292.
[3] Ma, Z. and S. Wang, Building energy research in Hong Kong: a review. Renewable and Sustainable Energy Reviews, 2009. 13(8): p. 1870-1883.
[4] Zheng, G., et al., Application of life cycle assessment (LCA) and extenics theory for building energy conservation assessment. Energy, 2009. 34(11): p. 1870-1879.
[5] Balcomb, J.D. and A. Curtner. Multi-criteria decision-making process for buildings. in Energy Conversion Engineering Conference and Exhibit, 2000.(IECEC) 35th Intersociety. 2000. IEEE.
[6] Praseeda, K., B.V. Reddy, and M. Mani, Embodied and operational energy of urban residential buildings in India. Energy and Buildings, 2016. 110: p. 211-219.
[7] Lolli, N., S.M. Fufa, and M. Inman, A parametric tool for the assessment of operational energy use, embodied energy and embodied material emissions in building. Energy Procedia, 2017. 111: p. 21-30.
[8] Azar, E. and C.C. Menassa, A comprehensive analysis of the impact of occupancy parameters in energy simulation of office buildings. Energy and buildings, 2012. 55: p. 841-853.
[9] Ibn-Mohammed, T., et al., Operational vs. embodied emissions in buildings—A review of current trends. Energy and Buildings, 2013. 66: p. 232-245.
[10] Karimpour, M., et al., Minimising the life cycle energy of buildings: Review and analysis. Building and Environment, 2014. 73: p. 106-114.
[11] Hsieh, T.-Y., S.-T. Lu, and G.-H. Tzeng, Fuzzy MCDM approach for planning and design tenders selection in public office buildings. International journal of project management, 2004. 22(7): p. 573-584.
[12] Saidur, R., et al., An end-use energy analysis in a Malaysian public hospital. Energy, 2010. 35(12): p. 4780-4785.
[13] Ramesh, T., R. Prakash, and K. Shukla, Life cycle energy analysis of buildings: An overview. Energy and buildings, 2010. 42(10): p. 1592-1600.
[14] Cabeza, L.F., et al., Life cycle assessment (LCA) and life cycle energy analysis (LCEA) of buildings and the building sector: A review. Renewable and sustainable energy reviews, 2014. 29: p. 394-416.
[15] Xu, J., et al., A systematic approach for energy efficient building design factors optimization. Energy and Buildings, 2015. 89: p. 87-96.
[16] Yang, Y., B. Li, and R. Yao, A method of identifying and weighting indicators of energy efficiency assessment in Chinese residential buildings. Energy Policy, 2010. 38(12): p. 7687-7697.
[17] Ali, H.H. and S.F. Al Nsairat, Developing a green building assessment tool for developing countries–Case of Jordan. Building and environment, 2009. 44(5): p. 1053-1064.
[18] Hygh, J.S., et al., Multivariate regression as an energy assessment tool in early building design. Building and environment, 2012. 57: p. 165-175.
[19] Pérez-Lombard, L., et al., A review of HVAC systems requirements in building energy regulations. Energy and buildings, 2011. 43(2-3): p. 255-268.
[20] Cole, R.J. and N. Larsson, GBTool user manual. Otawa: Green Building Challenge, 2002.
[21] Yang, Y.-l., H.-x. Tai, and T. Shi, Weighting indicators of building energy efficiency assessment taking account of experts’ priority. Journal of Central South University, 2012. 19(3): p. 803-808.
[22] Addy, M.N., E. Adinyira, and J. Ayarkwa, Identifying and weighting indicators of building energy efficiency assessment in Ghana. Energy Procedia, 2017. 134: p. 161-170.
[23] Li, H., et al., A Systematic Classification for HVAC Systems and Components. 2017, Pacific Northwest National Lab.(PNNL), Richland, WA (United States).
[24] Farah Habib, Z.B., Maryam Ghasabani, Prioritization of Effective Building Energy Consumption Parameters Using AHP (In Persian). Naghsh-e Jahan, 2014: p. 55-61.
[25] Norozian, N., Optimized pattern for energy efficiency assessment in Tehran buildings (In Persian). Naghsh-e Jahan, 2016: p. 65-77.
[26] Taylor, J.G. and S.D. Ryder, Use of the Delphi method in resolving complex water resources issues 1. JAWRA Journal of the American Water Resources Association, 2003. 39(1): p. 183-189.
[27] Pérez, V.L. and R. Schüler, The Delphi method as a tool for information requirements specification. Information & Management, 1982. 5(3): p. 157-167.
[28] Edwards, D., T. Griffin, and B. Hayllar, Urban tourism research: developing an agenda. Annuals of Tourism Research, 2008. 35(4): p. 1032-1052.
[29] Regulations, N.B., National building regulations, No. 3 (In Persian). Road and building ministry, 2012.