DOI QR코드

DOI QR Code

A Study on Classifying Building Energy Consumption Pattern Using Actual Building Energy Data

건물의 실측 에너지 데이터를 통한 건물 에너지 소비 패턴 분류에 관한 연구

  • Woo, Hei-Jee ;
  • Choi, Ki-Won ;
  • Kim, Hyeon Soo ;
  • Auh, Jin Sun ;
  • Cho, Soo Youn ;
  • Baek, Jumi ;
  • Kim, Gi-Seok ;
  • Leigh, Seung-Bok
  • 우혜지 ;
  • 최기원 ;
  • 김현수 ;
  • 어진선 ;
  • 조수연 ;
  • 백주미 ;
  • 김기석 ;
  • 이승복
  • Received : 2016.02.03
  • Accepted : 2016.05.04
  • Published : 2016.05.30

Abstract

The pattern of energy consumption in a building varies based on its characteristic features and the behavior of the occupants; therefore, it is difficult to classify buildings in terms of energy consumption. This study used only outdoor temperature and energy consumption as a parameter to analyze the energy consumption by a building, and thus the approach is different from the conventional methods that use complex computer simulations, data on energy consumptions related to heating cooling, and energy audits etc. First, raw data on the operational schedules of the buildings and internal-external dependency factor are developed as the primary analytical data. The preferred analytical data were categorized into four categories: internal-external factors, energy consumption, operational condition of the building, and energy consumption by outdoor temperature. A matrix that can be used as a relative indicator of a building's energy consumption in relation to its characteristics was also developed in this work. Using this energy pattern matrix, the obtained data could be used for retrofitting buildings, and a classification scheme based on the energy consumption pattern of buildings can be also prepared.

Keywords

Building Energy Consumption;Building Energy Pattern;Data-driven Method

References

  1. Kim, M. K., & Shin, D. H. (2012), A Study on Improvements of Policy System on School Building Retrofits for Energy Efficiency, Seoul urban research 13(3), 159-173
  2. Kim, Y. K., & Lee, T. W. (2012), An Analysis of the Energy Saving Effect Through the Retrofit and the Optimal Operation for HVAC Systems, Korean Journal of Air-Conditioning and Refrigeration Engineering, 24(4), 343-350 https://doi.org/10.6110/KJACR.2012.24.4.343
  3. Park, G. H., Kim, J. K., & Park, J. W. (2014), National and public school buildings Retrofit Study, Korea Energy Economics Institute, Basic research report 14-15
  4. Kim, K. H., & Haberl, J. S. (2015). Development of methodology for calibrated simulation in single-family residential buildings using three-parameter change-point regression model. Energy and Buildings, 99, 140-152. https://doi.org/10.1016/j.enbuild.2015.04.032
  5. Tardioli, G., Kerrigan, R., Oates, M., James, O. D., & Finn, D. (2015). Data Driven Approaches for Prediction of Building Energy Consumption at Urban Level, Energy Procedia, 78, 3378-3383. https://doi.org/10.1016/j.egypro.2015.11.754
  6. Kusiak, A., Li, M., & Zhang, Z. (2010). A data-driven approach for steam load prediction in buildings, Applied Energy, 87(3), 925-933. https://doi.org/10.1016/j.apenergy.2009.09.004
  7. Park, H. C., & Chung, M. (2009). Comparison of energy demand characteristics for hotel, hospital, and office buildings in korea, Korean Journal of Air-Conditioning and Refrigeration Engineering, 21(10), 553-558.
  8. Jung, K. T., Yoon, S. M., Moon, H. J., & Yeo, W. H. (2012). A Study on Building Energy Consumption Pattern Analysis Using Data Mining, KIEAE Journal, 12(2), 77-82.
  9. Park, K. H., & Kim, S. M. (2011). Analysis of energy consumption of buildings in the university, Korean Journal of Air-Conditioning and Refrigeration Engineering, 23(9), 633-638. https://doi.org/10.6110/KJACR.2011.23.9.633
  10. Cho, S. H. (2003). Effect of Measuring Period on Predicting the Annual Heating Energy Consumption for Building, Korean Journal of Air Conditioning and Refrigeration Engineering, 15(4), 287-294
  11. Jeong, S. H., Kim, H. Y., Lee, H. N., & Leigh, S. B. (2015). A Validation Study of Remote Energy Diagnosis Algorithm Performance through Actual Building Energy Data Analysis, Journal of the architectural institute of Korea planning & design, 31(5), 137-145.
  12. Abrahart, R. J., See, L. M., & Solomatine, D. P. (Eds.). (2008). Practical hydroinformatics: computational intelligence and technological developments in water applications, Springer Science & Business Media, 68, 17
  13. Mathieu, J. L., Price, P. N., Kiliccote, S., & Piette, M. A. (2011). Quantifying changes in building electricity use, with application to demand response, Smart Grid, IEEE Transactions on, 2(3), 507-518. https://doi.org/10.1109/TSG.2011.2145010
  14. Ali, M. T., Mokhtar, M., Chiesa, M., & Armstrong, P. (2011). A cooling change-point model of community-aggregate electrical load, Energy and Buildings, 43(1), 28-37. https://doi.org/10.1016/j.enbuild.2010.07.025

Acknowledgement

Supported by : 한국에너지기술평가원(KETEP)