Journal of the Architectural Institute of Korea Structure & Construction (대한건축학회논문집:구조계)

Architectural Institute of Korea (대한건축학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis of Annual Operation Status of Central Heating and Cooling System in a Public Office Building

공공건물 중앙식 냉난방시스템의 연간 운영 사례 분석

  • 라선중 ((주)한일엠이씨 기술연구소) ;
  • 엄태윤 ((주)한일엠이씨 기술연구소) ;
  • 손진웅 ((주)한일엠이씨 기술연구소)
  • Received : 2020.01.08
  • Accepted : 2020.02.11
  • Published : 2020.02.28
⟨ Previous Next ⟩

Abstract

The purpose of this study was to clarify precautions during the design and operation phases for energy reduction in a public office building. To check the operation status of the building, we measured the indoor temperature and humidity in the office space of the building installed central heating and cooling systems. And we analyzed these data and annual BEMS data. As a result, we found six problems related to decreasing system efficiency. Based on these, we presented the information to improve the efficiency of the system from the design and operation phase. Also, we present the need for a system to support the decision-making of operational managers in real-time for the energy efficiency of the building.

Keywords

Acknowledgement

Supported by : 한국에너지기술평가원 (KETEP)

본 연구는 산업통상자원부의 재원으로 한국에너지기술평가원 (KETEP)의 지원을 받아 수행한 연구 과제입니다.(No.20172010105610)

References

  1. Ahn, K. U., Park, S. H., Hwang, S., Choi, S., & Park, C. S. (2019). Optimal control strategies of eight parallel heat pumps using Gaussian process emulator. Journal of Building Performance Simulation, 12(5), 650-662. https://doi.org/10.1080/19401493.2019.1597924
  2. Ahn, Y. S., Kim, H. J., Lee. S. K., & Kim, B. S. (2019). Prediction of Heating Energy Consumption Using Machine Learning and Parameters in Combined Heat and Power Generation. Korean Journal of Air-Conditioning and Refrigeration Engineering, 31(8), 352-360. https://doi.org/10.6110/KJACR.2019.31.8.352
  3. Do, H., & Cetin, K. S. (2019). Data-Driven Evaluation of Residential HVAC System Efficiency Using Energy and Environmental Data. Energies, 12(1), 188. https://doi.org/10.3390/en12010188
  4. Hong, T., Yang, L., Hill, D., & Feng, W. (2014). Data and analytics to inform energy retrofit of high performance buildings. Applied Energy, 126, 90-106. https://doi.org/10.1016/j.apenergy.2014.03.052
  5. Jeong, Y. S., & Cho, C. H. (2016). A Forecast of Energy Demand in Building Sector using Energy System Model. Journal of KIAEBS, 10(5), 368-374.
  6. Kim, Y. K., & Lee, T. W. (2011). An Analysis of the Energy Saving Effects through Retrofit and Optimal Operation in Medium Sized Building. Proceeding of the SAREK summer Annual Conference, (), 654-659.
  7. Kim, Y. K., & Lee, T. W. (2018). An Analysis of the Energy Consumption Characteristics of a Medium Sized Office Building. Journal of Korea Facility Management Association, 9(1), 41-49.
  8. Kriksciuniene, D., Pitner, T., Kucera, A., & Sakalauskas, V. (2014). Data Analysis in the Intelligent Building Environment. IJCSA, 11(1), 1-17.
  9. Lim, H. M., Cho, S., Kim, G. S., & Seo, D. H. (2017). A Study on Empirical Study on the Improvement of BEMS Operation. Proceeding of the SAREK summer Annual Conference, (), 1076-1079.
  10. Park, B. Y., Cho, J. K., & Jeong Y. D. (2018). Research on Requirements of Power Demand Management for Integrated Building Energy Supply System. Korean Journal of Air-Conditioning and Refrigeration Engineering, 30(9), 443-450. https://doi.org/10.6110/KJACR.2018.30.9.443
  11. Park, S. H., Ahn, K. U., Hwang, S., Choi, S., & Park, C. S. (2019). Machine learning vs. hybrid machine learning model for optimal operation of a chiller. Science and Technology for the Built Environment, 25(2), 209-220. https://doi.org/10.1080/23744731.2018.1510270
  12. Ra, S. J., Son, J. W., & Aum, T. Y. (2019). An Analysis on Operation Status using BEMS Data of the HVAC Systems in a Public Office Building. Proceeding of the SAREK 2019 Summer Annual Conference, (), 321-324.
  13. Seo, J. S., Kim, Y. K., Yu, J. H., & Lee, T. W. (2012). An Analysis of Energy Consumption Partterns and Operation Status for a Medium-sized Office Building. Proceeding of the SAREK Winter Annual Conference, (), 117-120.
  14. Suh, W. J., Chu, H. G., S, H. S., & Ra, S. J. (2017). Overall Efficiency Optimization of a Buildings's Cooling Plant using Deep Neural Network. Proceeding of the Autumn Annual Conference of AIK, 37(2), 552-553.