에너지경제연구 Korean Energy Economic Review Volume 14, Number 3, November 2015 : pp. 229~264 GCAM-EML 을이용한대형상업용 건물에너지효율변화의장기영향분석 * 229
230
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~ ~ ~ 232
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ln 239
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[ 그림 1] 포화수요를계산하기위한총건물면적, 인구, 노동참여인구의추이 241
< 표 1> 기준년도건물면적 (2010 년 ) 242
243
< 표 2> 대형상업용건물의단위면적당년간에너지사용량분포추이 kwh 244
[ 그림 2] 대형상업용건물단위면적당연간에너지사용량 (kwh/ 년 ) 분포 245
< 표 3> 대형상업용건물에적용하는 9 개시나리오의내용 246
247
< 표 4> 시나리오구성요소입력값 ± 248
[ 그림 3] 단위면적당 1 차에너지소비량변화 (2015~2055) 249
250
[ 그림 4] 기준안대비년도별건물총에너지절감량및누적량 ( 단위 : Bil. KWh, MTOE) 251
252
< 표 5> 에너지소비량측면에서본조명기술의시장점유율변화 253
254
접수일 (2015 년 8 월 27 일 ), 수정일 (2015 년 11 월 23 일 ), 게재확정일 (2015 년 11 월 27 일 ) 255
. 2014. 5 ().. [ 4]. 2012., 2012-1473.. 2013a. 2013.. 2015. ( 15 1/4),.. 2013b., 2013-248.,,,. 2012. " ",, Vol. 12, No. 3.. 2015. 2014. 2014., 2014-16.. 2013..., http://www.kemco.or.kr/building/ v2/buil_cert/ buil_cert_4_1.asp.., http://www.kemco.or.kr/building/v2/.. 2010., 10-05.. 2012. 2011.. 2010... 2012. IEA, 12-09. 256
. 2011. : 2010~2060, 2011.12.7.. 2014.. Brenkert AL, SH Kim, AJ Smith and HM Pitcher. 2003. Model Documentation for the MiniCAM. PNNL-14337. Chaturvedi, Vaibhav, Jiyong Eom, Leon E. Clarke and Priyadarshi R. Shukla. 2014. "Long term building energy demand for India: Disaggregating end use energy services in an integrated assessment modeling framework". Energy Policy Volume 64 : pp226242. Clarke, John F., J.A. Edmonds 1993. Modelling energy technologies in a competitive market Energy Economics. Volume 15. Issue 2. Pages 123-129 Eom., J.. 2013. Introduction to the GCAM's Building Energy Model. presented at Ajou University. Eom, J., GP Kyle, LE Clarke, PL Patel, and SH Kim. 2012. China s Building Energy Use: A Long-Term Perspective based on a Detailed Assessment. Pacific Northwest National Laboratory. PNNL-21073. Energy Modeling Lab. 2014. Documentation of Building Sector in Global Change Assessment Model. Ajou University GCAM Buildings. http://wiki.umd.edu/gcam/index.php/buildings GCAM wiki homepage. https://wiki.umd.edu/gcam/index.php/main_page Intergovernmental Panel on Climate Change(IPCC). 2007. Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [B. Metz, O.R. Davidson, P.R. Bosch, R. Dave, L.A. Meyer (eds)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. International Energy Agency(IEA). Beyond 2020 Professional Browser.. 2011. 25 Energy Efficiency Policy Recommendations 2011 Update Joint Global Change Research Institute(JGCRI) / Pacific Northwest National 257
Laboratory(PNNL). 2014. "GCAM 4.0 source code". available at:http://www.globalchange. umd.edu/models/gcam/download/ Kyle, Page and Son H. Kim. 2011. "Long-term implications of alternative light-duty vehicle technologies for global greenhouse gas emissions and primary energy demands". Energy Policy Volume 39 Issue 5. pp3012-3024. Kyle, Page. 2010. Documentation of GCAM Calibration. Pacific Northwest National Laboratory internal material. Maurer EP, Adam JC, Wood AW, 2009, Climate model based consensus on the hydrologic impacts of climate change to the Rio Lempa basin of Central America. Hydrol Earth Syst Sci 13 (2):183-194. doi:10.5194/hess-13-183-2009 Mishra, Gouri Shankar, Jacob Teter, Geoffrey M. Morrison, Sonia Yeh, Page Kyle and Son H. Kim. 2013. Transportation Module of Global Change Assessment Model (GCAM) Model Documentation Version 1.0. Institute of Transportation Studies at University of California, Davis, Research Report UCD-ITS-RR-13-05. OECD/IEA. 2005. Energy Statistics MANUAL. Scott, MJ, RH Moss, DS Daly, PL Patel, J Hathaway, MJ Peterson, CS Lansing, JS Rice, Y Liu, Y Zhou, HC McJeon. 2014. Calculating Impacts of Energy Standards on Energy Demand in U.S. Buildings under Uncertainty with an Integrated Assessment Model: Technical Background Data, Pacific Northwest National Laboratory. PNNL-23918. United Nations Environment Programme Sustainable Buildings and Climate Initiative. Why Buildings. available at: http://www.unep.org/sbci/ AboutSBCI/ Background.asp accessed: 2015.08.03. Wise, Marshall, G. Page Kyle, James J. Dooley, and Son H. Kim. 2010. "The impact of electric passenger transport technology under an economy-wide climate policy in the United States: Carbon dioxide emissions, coal use, and carbon dioxide capture and storage". International Journal of Greenhouse Gas 258
Control Volume 4 Issue 2. pp301-308. Yurnaidi, Zulfikar,,,. 2015. "Analysis of Buildings Lighting Technological Change and Its Impact on the Energy System". 2015 3 B, B103. 2015.2.24. 15:00~16:30. Yu, Sha, Jiyong Eom, Yuyu Zhou, Meredydd Evans and Leon Clarke. 2014. "Scenarios of building energy demand for China with a detailed regional representation". Energy Volume 67. pp284297. Yu, Sha, Jiyong Eom, Yuyu Zhou, Meredydd Evans and Leon Clarke. "A long-term, integrated impact assessment of alternative building energy code scenarios in China". Energy Policy, Volume 67 issue C. pp626-639. Zhou, Yuyu, Leon Clarke, Jiyong Eom, Page Kyle, Pralit Patel, Son H. Kim, James Dirks, Erik Jensen, Ying Liu, Jennie Rice, Laurel Schmidt and Timothy Seiple. 2014. "Modeling the effect of climate change on U.S. state-level buildings energy demands in an integrated assessment framework". Applied Energy 113. pp10771088. Zhou, Yuyu, Jiyong Eom, Leon Clarke. 2013. The effect of global climate change, population distribution, and climate mitigation on building energy use in the U.S. and China. Climatic Change, Volume 119, Issue 3, pp 979-992. 259
< 부표 1> 에너지총조사부문별에너지소비량 < 부표 2> GCAM 과 GCAM-EML 의모형세분화 260
< 부표 3> kwh 이상제외된표본의내용 (2011 년기준 ) 261
< 부표 4> 시나리오별단위면적당 1 차에너지 (kwh/m2) 저감효과 262
ABSTRACT While various energy efficiency improvement programs with specific energy saving targets are being proposed by governments, the feasibility of those programs and targets are properly assessed in quantitative manner. This research focuses on the enhanced building shell efficiency, efficiency improvement of lighting appliances to reduce cooling energy demand via 9 scenarios discussed in this article using GCAM-EML. Provided in this process of research are quantification of such policy impacts on energy system through scenario simulation results. The results show that this energy efficiency of large buildings are evaluated to be improved at maximum of 4.1% and 8.5% by 2030 and 2055 compared to the reference case. Although this improvement does not seem be large enough, it should be noted that the cumulative energy savings up to 2030 is estimated to be 2.36MTOE and it is 1.16 times of large commercial building energy consumption of 2010 reported by energy survey conducted on 2011. A further detailed research with a more realistic scenarios would better describe how various energy efficiency improvement programs currently deployed affect the overall building s energy efficiency in the future, and this information could be better utilized for the detailed design of various ongoing energy efficiency programs. 263
Key Words : Building Energy Labels or Certificates, Large Commercial Building, Energy Consumption Survey, Energy Efficiency, GCAM-EML 264