대한건축학회논문집 (Journal of the Architectural Institute of Korea)

  • 제38권5호
  • /
  • Pages.139-146
  • /
  • 2022
  • /
  • 2733-6239(pISSN)
  • /
  • 2733-6247(eISSN)
대한건축학회 (Architectural Institute of Korea)
권호 표지
DOI QR코드

DOI QR Code

월별 에너지 사용량 패턴 분석을 통한 업무시설 에너지 절감 방안 도출

Development of Energy Conservation Measures for Office Buildings by Analyzing Monthly Energy Use Patterns

  • 오지현 (아주대학교 스마트융합건축학과) ;
  • 김혜기 (아주대학교 공학연구소) ;
  • 최병주 (아주대학교 건축학과) ;
  • 김선숙 (아주대학교 건축학과)
  • Oh, Ji-Hyun (Dept. of Smart Convergence Architecture, Ajou University) ;
  • Kim, Hye-Gi (Engineering Research Institute, Ajou University) ;
  • Choi, Byung-Ju (Dept. of Architectural Engineering, Ajou University) ;
  • Kim, Sun-Sook (Dept. of Architectural Engineering, Ajou University)
  • 투고 : 2022.01.26
  • 심사 : 2022.05.06
  • 발행 : 2022.05.30
⟨ 이전 논문 다음 논문 ⟩

초록

To achieve carbon neutrality in a city or country, it is required to evaluate energy performance and energy conservation measures for large buildings. The energy performance of existing buildings are widely evaluated by annual EUI (energy use intensity, kWh/m2yr). However, this annual value have limitations on analyzing seasonal effect and establishing energy conservation strategies. In this paper, we analyze monthly energy use patterns of large buildings and proposed general energy conservation strategies and measures according to the patterns. To classify the energy use patterns, we investigated clustering techniques on monthly energy use of office buildings in Korea. A k-means algorithm was implemented, and two different methods were compared: feature based k-means and time series k-means. The methods were performed with Euclidean distance metric and we tested our methods on energy use data from national database. The results show that feature based k-means method is significant in energy use pattern analysis. The energy use patterns of office buildings were divided into five clusters. We analyzed the characteristics of clusters by building size, annual and seasonal energy use.

키워드

과제정보

이 연구는 2020년도 한국연구재단 연구비 지원에 의한 결과의 일부임. 과제번호:NRF2020R1A2C110303311

참고문헌

  1. Alam, M., & Devjani, M. R. (2021). Analyzing energy consumption patterns of an educational building through data mining. Journal of Building Engineering, 44, 103385. doi:http://doi.org.ssl.openlink.ajou.ac.kr/10.1016/j.jobe.2021.103385
  2. ASHRAE. (2014). Advanced Energy Design Guide for Small to Medium Office Buildings: achieving 50% energy savings toward a net zero energy building, Atlanta: American Society of Heating, Refrigerating and Air Conditioning Engineers, Inc.
  3. Bourdeau, M., Basset, P., Beauchene, S., Da Silva, D., Guiot, T., Werner, D., & Nefzaoui, E. (2021). Classification of daily electric load profiles of non-residential buildings. Energy and Buildings, 233, 110670. https://doi.org/10.1016/j.enbuild.2020.110670
  4. Choi. S.B., Yoon, S.M., & Kim, Y.S. (2021). Urban building energy pattern analysis using clustering approaches with open data sources. SAREK Summer Annual Conference, 903-907.
  5. DOE. (2011). Advanced Energy Retrofit Guide-Office Bui ldings. U.S. Department of Energy. Retrieved December 3 0, 2021 from https://www.energy.gov/eere/buildings/advanced-energy-retrofit-guides.
  6. IBE. (2013). LEAN Energy Analysis Using Regression Analysis to Assess Building Energy Performance. Institute for building efficiency. Retrieved December 30, 2021 from https://buildingefficiencyinitiative.org/resources/lean-energy-analysis
  7. Kim, D.W., Yoon, S.H., & Park, C.S. (2014). Simple energy benchmarking for existing office buildings. Journal of the Architectural Institute of Korea Planning and Design, 30(9), 223-234. https://doi.org/10.5659/JAIK_PD.2014.30.9.223
  8. Kim, H. G., Kim, H. J., Jeon, C. H., Chae, M. W., Cho, Y. H., & Kim, S. S. (2020). Analysis of energy saving effect and cost efficiency of ECMs to upgrade the building energy code. Energies, 13(18), 4955. https://doi.org/10.3390/en13184955
  9. Kim, H.G. (2017). Development of an Energy Performance Benchmark Using Quantitative Analysis of Energy Consumption of Office Buildings, Thesis, Ajou University.
  10. Kim, S.H., & Lee, J.W. (2021). Electricity Consumption Pattern Analysis of Nationwide Apartment using Clustering Method based on Open Data. Journal of Korean Institute of Architectural Sustainable Environment and Building Systems, 15(5), 537-548.
  11. Ko, Y.D., Yi, D.H., Yoo, Y.S., Jo, H.G., Kim, D.W., & Park, C.S. (2020). Repeatability of monthly energy use for 10,750 buildings. Autumn Annual Conference of AIK, 2020, 145-147.
  12. Lee, Y.J., Ko, M.J., & Choi, D.S. (2019). Pattern and energy intensity analysis of monthly gas energy consumption in apartment using dynamic time warping hierarchical clustering. Journal of The Korean Society of Living Environmental System, 26(1), 134-139. https://doi.org/10.21086/ksles.2019.02.26.1.134
  13. Qian, D., Li, Y., Niu, F., & O'Neill, Z. (2019). Nationwide savings analysis of energy conservation measures in buildings. Energy Conversion and Management, 188, 1-18. https://doi.org/10.1016/j.enconman.2019.03.035
  14. Quintana, M., Arjunan, P., & Miller, C. (2019). Islands of misfit buildings: detecting uncharacteristic electricity use behavior using load shape clustering. Building Simulation, 14(1), 119-130. https://doi.org/10.1007/s12273-020-0626-1
  15. GROK. (2020). 2050 Carbon Neutral Strategy of the Republic of Korea. The Government of the Republic of Korea. Retrieved October 7, 2021 from https://www.moef.go.kr/.
  16. Yang, J., Ning, C., Deb, C., Zhang, F., Cheong, D., Lee, S. E., Sekhar, C., & Tham, K. W. (2017). K-shape clustering algorithm for building energy usage patterns analysis and forecasting model accuracy improvement. Energy and Buildings, 146, 27-37. doi: http://doi.org.ssl.openlink.ajou.ac.kr/10.1016/j.enbuild.2017.03.071
  17. Yoon, Y.R., Shin, S.H., & Moon, H.J. (2017). Analysis of building energy consumption patterns according to building types using clustering methods. Journal of The Korean Society of Living Environmental System, 24(2), 232-237. https://doi.org/10.21086/ksles.2017.04.24.2.232