All Issue

2021 Vol.15, Issue 6 Preview Page

Research Article

Comparison of Energy Consumption Depending on the Occupancy-based HVAC Control in a University Campus Building

재실 기반 설비제어에 따른 대학 건물의 에너지소비량 비교 분석

Nam-Chul Seong1
,
Goo-Pyo Hong2*
성 남철1
,
홍 구표2*
1Senior Researcher, Department of Architectural Engineering, Kangwon National University, Samcheok, Korea
2Assistant Professor, Department of Architectural Engineering, Kangwon National University, Samcheok, Korea
1강원대학교 건설융합학부 선임연구원
2강원대학교 건설융합학부 조교수
*Corresponding Author
30 December 2021. pp. 646-658
PDF XML Citation
Abstract

In this study, occupant information, one of the information that can be found in the operation stage of the building, was collected using an actual survey. After converting occupancy data to HVAC system operation schedule, it was analyzed and compared the change of energy consumption applied with occupancy-based HVAC control. As a result, the actual number of occupants was smaller than the case where the occupancy density per area was set, so the energy consumption of the total building decreased by 15.16%, and the energy consumption of the HVAC system decreased by 34.32%. When the occupanct-based HVAC control was applied, the energy consumption of the entire building decreased by 9.71%, and the energy consumption of the HVAC system decreased by 28.42%. As a result of analyzing energy consumption by system, the control of occupancy-based HVAC control has a greater energy saving effect on heating than cooling. The occupancy-based HVAC control is simpler than other controls and has a significant energy saving effect.

Keywords
Occupancy schedule
Occupancy data
Occupancy-based HVAC control
Energy consumption
Simulation
University campus building
키워드
재실 스케줄
재실 정보
재실 기반 설비 제어
에너지소비량
시뮬레이션
대학 건물
References
1
Anand, P., Sekhar, C., Cheong, D., Santamouris, M., Kondepudi, S. (2019). Occupancy- based zone-level VAV system control implications on thermal comfort, ventilation, indoor air quality and building energy efficiency. Energy and Buildings, 204, 109473. 10.1016/j.enbuild.2019.109473
2
Capozzoli, A., Piscitelli, M.S., Gorrino, A., Ballarini, I., Corrado, V. (2017). Data analytics for occupancy pattern learning to reduce the energy consumption of HVAC systems in office buildings. Sustainable Cities and Society, 35, 191-208. 10.1016/j.scs.2017.07.016
3
Davis III, J.A., Nutter, D.W. (2010). Occupancy diversity factors for common university building types. Energy and buildings, 42(9), 1543-1551. 10.1016/j.enbuild.2010.03.025
4
de Bakker, C., Aries, M., Kort, H., Rosemann, A. (2017). Occupancy-based lighting control in open-plan office spaces: A state-of-the-art review. Building and Environment, 112, 308-321. 10.1016/j.buildenv.2016.11.042
5
Ekwevugbe, T., Brown, N., Pakka, V., Fan, D. (2017). Improved occupancy monitoring in non-domestic buildings. Sustainable Cities and Society, 30, 97-107. 10.1016/j.scs.2017.01.003
6
Emery, A.F., Kippenhan, C.J. (2006). A long term study of residential home heating consumption and the effect of occupant behavior on homes in the Pacific Northwest constructed according to improved thermal standards. Energy, 31(5), 677-693. 10.1016/j.energy.2005.04.006
7
Feng, X., Yan, D., Hong, T. (2015). Simulation of occupancy in buildings. Energy and Buildings, 87, 348-359. 10.1016/j.enbuild.2014.11.067
8
Guo, X., Tiller, D.K., Henze, G.P., Waters, C.E. (2010). The performance of occupancy- based lighting control systems: A review. Lighting Research & Technology, 42(4), 415-431. 10.1177/1477153510376225
9
Kim, J.H., Seong, N.C., Choi, W. (2019). Cooling load forecasting via predictive optimization of a nonlinear autoregressive exogenous (NARX) neural network model. Sustainability, 11(23), 6535. 10.3390/su11236535
10
Kim, J.H., Seong, N.C., Choi, W. (2020). Forecasting the Energy Consumption of an Actual Air Handling Unit and Absorption Chiller Using ANN Models. Energies, 13(17), 4361. 10.3390/en13174361
11
Liang, X., Hong, T., Shen, G.Q. (2016). Occupancy data analytics and prediction: A case study. Building and Environment, 102, 179-192. 10.1016/j.buildenv.2016.03.027
12
Masoso, O.T., Grobler, L.J. (2010). The dark side of occupants' behaviour on building energy use. Energy and buildings, 42(2), 173-177. 10.1016/j.enbuild.2009.08.009
13
Seong, N.C., Kim, J.H., Choi, W. (2019). Optimal control strategy for variable air volume air-conditioning systems using genetic algorithms. Sustainability, 11(18), 5122. 10.3390/su11185122
14
Staats, H., van Leeuwen, E., Wit, A. (2000). A longitudinal study of informational interventions to save energy in an office building. Journal of Applied Behavior Analysis, 33(1), 101-104. 10.1901/jaba.2000.33-10110738959PMC1284229
15
Wang, S., Zhou, Q., Xiao, F. (2010). A system-level fault detection and diagnosis strategy for HVAC systems involving sensor faults. Energy and Buildings, 42(4), 477-490. 10.1016/j.enbuild.2009.10.017
16
Yang, J., Santamouris, M., Lee, S.E. (2016). Review of occupancy sensing systems and occupancy modeling methodologies for the application in institutional buildings. Energy and Buildings, 121, 344-349. 10.1016/j.enbuild.2015.12.019
Information
  • Publisher :Korean Institute of Architectural Sustainable Environment and Building Systems
  • Publisher(Ko) :한국건축친환경설비학회
  • Journal Title :Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
  • Journal Title(Ko) :한국건축친환경설비학회논문집
  • Volume : 15
  • No :6
  • Pages :646-658
  • Received Date : 2021-11-02
  • Revised Date : 2021-12-15
  • Accepted Date : 2021-12-19
  • DOI :https://doi.org/10.22696/jkiaebs.20210054
Journal Informaiton Journal of Korean Institute of Architectural Sustainable Environment and Building Systems Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
Manuscript Submission
  • NRF
  • KOFST
  • KISTI Current Status
  • KISTI Cited-by
  • crosscheck
  • orcid
  • open access
  • ccl
Journal Informaiton Journal Informaiton - close