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Research Article

Influence of household air-conditioning use modes on the energy performance of residential district cooling systems

Xin Zhou1( ), Da Yan2, Xiaohang Feng2, Guangwei Deng3, Yiwen Jian4, Yi Jiang2
School of Architecture, Southeast University, Nanjing 210096, China
School of Architecture, Tsinghua University, Beijing 100084, China
Shanghai Research Institute of Building Sciences Group, Shanghai 610041, China
Beijing University of Technology, Beijing 100084, China
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Abstract

During technical evaluations of cooling systems in residential buildings, it is necessary to consider the influence of the household air-conditioning (AC) use modes. In other words, how the occupants control the AC, for instance, when it is turned on, what the temperature setting is, and how long it is used. Field measurements and spot interviews indicate that AC usage by residents should be environmental, event and random related. A reduced-order AC conditional probability (CP) model was developed in this study to describe AC usage. The AC CP model was integrated with a building energy modeling program (BEMP) to reflect the interaction of the AC operation and the indoor environment. With consideration of stochastic AC use modes, the uncertainty of user compositions was studied. Additionally, simulation results revealed that AC use modes and user compositions can cause up to a 4.5-fold difference in the system efficiency of district cooling systems. The Lorenz curve and Gini coefficient were applied in this study to describe the load distribution in a quantitative manner. Through a comparison with the constant schedule definition model, the study also identified inclusion of the stochastic feature of AC use modes and their compositions in simulations as being important to the technical evaluation of district cooling systems.

Keywords

load distributionenergy performanceuncertainty analysisAC usage modelresidential district cooling system

References

 
A Al-Qattan, A ElSherbini, K Al-Ajmi (2014). Solid oxide fuel cell application in district cooling. Journal of Power Sources, 257: 21-26.
Crossref Google Scholar
 
AM Araujo, S Roberto, EL La Rovere (2001). The potential for electricity conservation and peak load reduction in the residential sector of Brazil. Energy, 26: 413-429.
Crossref Google Scholar
 
ASHRAE (2010). ASHRAE Standard 55-2010, Thermal Environmental Conditions for Human Occupancy. Atlanta, GA, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.
 
B Boardman (2004). Achieving energy efficiency through product policy: The UK experience. Environmental Science & Policy, 7: 165-176.
Crossref Google Scholar
 
Building Energy Research Center in Tsinghua University (2013). 2013 Annual Report on China Building Energy Efficiency. Beijing: China Architecture & Building Press. (in Chinese)
 
SQ Chen, N Li, J Guan, Y Xie, F Sun, J Ni (2008). A statistical method to investigate national energy consumption in the residential building sector of China. Energy and Buildings, 40: 654-665.
Crossref Google Scholar
 
X Chen, GQ Zhang, LK Yu (2001). Life-cycle cost analysis applied to comparison among different air conditioning schemes for residential district. In: Proceedings of 4th International Conference on Indoor Air Quality, Ventilation and Energy Conservation in Buildings, Changsha, China, pp. 1863-1869.
 
TT Chow, WH Au, R Yau, V Cheng, A Chan,, KF Fong (2004a). Applying district-cooling technology in Hong Kong. Applied Energy, 79: 275-289.
Crossref Google Scholar
 
TT Chow, ALS Chan, CL Song (2004b). Building-mix optimization in district-cooling system implementation. Applied Energy, 77: 1-13.
Crossref Google Scholar
 
TT Chow, KF Fong, ALS Chan, R Yau, WH Au, V Cheng (2004c). Energy modeling of district cooling system for new urban development. Energy and Buildings, 36: 1153-1162.
Crossref Google Scholar
 
G Corrado (1921). Measurement of Inequality of Incomes. The Economic Journal, 31: 124-126.
Crossref Google Scholar
 
S D'Oca, T Hong (2015). Occupancy schedules learning process through a data mining framework. Energy and Buildings, 88: 395-408.
Crossref Google Scholar
 
RJ de Dear, GS Brager (2002). Thermal comfort in naturally ventilated buildings: Revision to ASHRAE standard 55. Energy and Buildings, 34: 549-561.
Crossref Google Scholar
 
S Devotta, AS Padalkar, NK Sane (2005). Performance assessment of HC-290 as a drop-in substitute to HCFC-22 in a window air conditioner. International Journal of Refrigeration, 28: 594-604.
Crossref Google Scholar
 
PO Fanger (1970). Thermal Comfort: Analysis and Applications in Environmental Engineering. New York: McGraw-Hill.
 
X Feng, D Yan, T Hong (2015). Simulation of occupancy in buildings. Energy and Buildings, 87: 348-359.
Crossref Google Scholar
 
M Frontczak, P Wargocki (2011). Literature survey on how different factors influence human comfort in indoor environments. Building and Environment, 46: 922-937.
Crossref Google Scholar
 
P Hoes, JLM Hensen, MGLC Loomans, B de Vries, D Bourgeois (2009). User behavior in whole building simulation. Energy and Buildings, 41: 295-302.
Crossref Google Scholar
 
ISO (2005). ISO 7730, Ergonomics of the Thermal Environment—Analytical Determination and Interpretation of Thermal Comfort Using Calculation of the PMV and PPD Indices and Local Thermal Comfort Criteria.
 
ZX Jing, XS Jiang, QH Wu, WH Tang, B Hua (2014). Modelling and optimal operation of a small-scale integrated energy based district heating and cooling system. Energy, 73: 399-415.
Crossref Google Scholar
 
B Jones (2000). Capabilities and limitations of thermal models, Smart Controls and Thermal Comfort Project. Final Report (Confidential). Oxford Brookes University, pp. 112-121.
 
WA Kamal (1997). Improving energy efficiency—The cost-effective way to mitigate global warming. Energy Conversion and Management, 38: 39-59.
Crossref Google Scholar
 
I Korolija, L Marjanovic-Halburd, Y Zhang, VI Hanby (2011). Influence of building parameters and HVAC systems coupling on building energy performance. Energy and Buildings, 43: 1247-1253.
Crossref Google Scholar
 
JC Lam, KKW Wan, CL Tsang, L Yang (2008). Building energy efficiency in different climates. Energy Conversion and Management, 49: 2354-2366.
Crossref Google Scholar
 
M Lenzena, M Wier,, C Cohen, H Hayami, S Pachauri, R Schaeffer (2006). A comparative multivariate analysis of household energy requirements in Australia, Brazil, Denmark, India and Japan. Energy, 31: 181-207.
Crossref Google Scholar
 
WD Long, T Zhong, BH Zhang (2003). Situation and trends of residential building environment services in Shanghai. In: Proceedings of 4th International Symposium on Heating, Ventilating and Air Conditioning. Beijing: Tsinghua University Press, pp. 493-498.
 
S Meyers, JE McMahon, M McNeil, X Liu (2003). Impacts of US federal energy efficiency standards for residential appliances. Energy, 28: 755-767.
Crossref Google Scholar
 
N Morelli (2001). Technical innovation and resource efficiency. A model for Australian household appliances. The Journal of Sustainable Product Design, 1: 3-17.
Crossref Google Scholar
 
T Nagota, Y Shimoda, M Mizuno (2008). Verification of the energy-saving effect of the district heating and cooling system—Simulation of an electric-driven heat pump. Building and Environment, 40: 732-741.
Crossref Google Scholar
 
JF Nicol (2001). Characterising occupant behaviour in buildings: Towards a stochastic model of occupant use of windows, lights, blinds, heaters and fans. In: Proceedings of International IBPSA Building Simulation Conference, Rio de Janeiro, Brazil, pp. 493-498.
 
J O'Doherty, S Lyons, RSJ Tol (2008). Energy-using appliances and energy-saving features: Determinants of ownership in Ireland. Applied Energy, 85: 650-662.
Crossref Google Scholar
 
J Ortiga, JC Bruno, A Coronas (2013). Operational optimisation of a complex trigeneration system connected to a district heating and cooling network. Applied Thermal Engineering, 50: 1536-1542.
Crossref Google Scholar
 
J Page, D Robinson, N Morel, J-L Scartezzini (2008). A generalised stochastic model for the simulation of occupant presence. Energy and Buildings, 40, 2: 83-98.
Crossref Google Scholar
 
N Pardo, K Vatopoulos, AK Riekkola, A Perez (2013). Methodology to estimate the energy flows of the European Union heating and cooling market. Energy, 52: 339-352.
Crossref Google Scholar
 
L Peeters, R de Dear, J Hensen, W D'haeseleer (2009). Thermal comfort in residential buildings: Comfort values and scales for building energy simulation. Applied Energy, 86: 772-780.
Crossref Google Scholar
 
C Peng, D Yan, R Wu, C Wang, X Zhou, Y Jiang (2012). Quantitative description and simulation of human behavior in residential buildings. Building Simulation, 5: 85-94.
Crossref Google Scholar
 
L Pérez-Lombard, J Ortiz, C Pout (2008). A review on buildings energy consumption information. Energy and Buildings, 40: 394-398.
Crossref Google Scholar
 
CF Reinhart (2004). Lightswitch-2002: A model for manual and automated control of electric lighting and blinds. Solar Energy, 77: 15-28.
Crossref Google Scholar
 
X Ren, Y Da, C Wang (2014). Air-conditioning usage conditional probability model for residential buildings. Building and Environment, 81: 172-182.
Crossref Google Scholar
 
B Rezaie, MA Rosen (2012). District heating and cooling: Review of technology and potential enhancements. Applied Energy, 93: 2-10.
Crossref Google Scholar
 
I Richardson, M Thomson, D Infield (2008). A high-resolution domestic building occupancy model for energy demand simulations. Energy and Buildings, 40, 8: 1560-1566.
Crossref Google Scholar
 
HB Rijal, P Tuohy, MA Humphreys, JF Nicol, A Samuel, J Clarke (2007). Using results from field surveys to predict the effect of open windows on thermal comfort and energy use in buildings. Energy and Buildings, 39: 823-836.
Crossref Google Scholar
 
Y Shimoda, T Nagota, N Isayama, M Mizuno (2008). Verification of energy efficiency of district heating and cooling system by simulation considering design and operation parameters. Building and Environment, 43: 569-577.
Crossref Google Scholar
 
J Soederman (2007). Optimisation of structure and operation of district cooling networks in urban regions. Applied Thermal Engineering, 27: 2665-2676.
Crossref Google Scholar
 
J Tanimoto, A Hagishima, H Sagara (2008). A methodology for peak energy requirement considering actual variation of occupants' behavior schedules. Building and Environment, 43: 610-619.
Crossref Google Scholar
 
T Ueno, F Sano, O Saeki, K Tsuji (2006). Effectiveness of an energy-consumption information system on energy savings in residential houses based on monitored data. Applied Energy, 83: 166-183.
Crossref Google Scholar
 
C Wang, D Yan, Y Jiang (2011). A novel approach for building occupancy simulation. Building Simulation, 4:149-167.
Crossref Google Scholar
 
S Wei, R Jones, P de Wilde (2014). Driving factors for occupant-controlled space heating in residential buildings. Energy and Buildings, 70: 36-44.
Crossref Google Scholar
 
W Weibull (1951). A statistical distribution function of wide applicability. ASME Journal of Applied Mechanics, 18, 3: 293-297.
Crossref Google Scholar
 
D Yan, J Xia, W Tang, F Fangting Song, X Zhang, Y Jiang (2008). DeST—An integrated building simulation toolkit, Part Ⅰ: Fundamentals. Building Simulation, 1: 95-110.
Crossref Google Scholar
 
D Yan, W O'Brien, T Hong, X Feng, HB Gunay, F Tahmasebi, A Mahdavi (2015). Occupant behavior modeling for building performance simulation: Current state and future challenges. Energy and Buildings, 107: 264-278.
Crossref Google Scholar
 
H Yoshino, Y Yoshino, Q Zhang, A Mochida, N Li, Z Li, H Miyasaka (2006). Indoor thermal environment and energy saving for urban residential buildings in China. Energy and Buildings, 38: 1308-1319.
Crossref Google Scholar
 
X Zhang, J Xia, Z Jiang, J Huang, R Qin, Y Zhang, Y Liu, Y Jiang (2008). DeST—An integrated building simulation toolkit, Part Ⅱ: Applications. Building Simulation, 1: 193-209.
Crossref Google Scholar
 
WJ Zhang, LL Huai, JL Liu (2009). Comparison analysis of energy consumption characteristics between decentralized and centralized AC systems in residential buildings by commissioning. In: Proceedings of 5th International Workshop on Energy and Environment of Residential Buildings and 3rd International Conference on Built Environment and Public Health, Guilin, China, pp. 1768-1775.
 
J Zhao, B Lasternas, KP Lam, R Yun, V Loftness (2014). Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining. Energy and Buildings, 82: 341-355.
Crossref Google Scholar
 
X Zhou, T Hong, D Yan (2013). Comparison of building energy modeling programs: HVAC systems. Available at http://www.osti.gov/scitech/biblio/1165199. Accessed May 2015.
Crossref
 
X Zhou, T Hong, D Yan (2014). Comparison of HVAC system modelling in EnergyPlus, DeST and DOE-2.1E. Building Simulation, 7: 21-33.
Crossref Google Scholar
 
D Zhu, D Yan, C Wang, T Hong (2013). A detailed loads comparison of three building energy modeling programs: EnergyPlus, DeST and DOE-2.1E. Building Simulation, 6: 323-335.
Crossref Google Scholar
Building Simulation
Volume 9 Issue 4,
August 2016
Pages 429-441
DOI: 10.1007/s12273-016-0280-9
Cite this article:
Zhou X, Yan D, Feng X, et al. Influence of household air-conditioning use modes on the energy performance of residential district cooling systems. Building Simulation, 2016, 9(4): 429-441. https://doi.org/10.1007/s12273-016-0280-9

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Received: 22 November 2015
Revised: 19 January 2016
Accepted: 22 January 2016
Published: 11 March 2016
© Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2016
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