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

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  • Building Systems and Components
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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.

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References

  • Al-Qattan A, El Sherbini A, Al-Ajmi K (2014). Solid oxide fuel cell application in district cooling. Journal of Power Sources, 257: 21–26.

    Article  Google Scholar 

  • Araujo AM, Roberto S, La Rovere EL (2001). The potential for electricity conservation and peak load reduction in the residential sector of Brazil. Energy, 26: 413–429.

    Article  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.

    Google Scholar 

  • Boardman B (2004). Achieving energy efficiency through product policy: The UK experience. Environmental Science & Policy, 7: 165–176.

    Article  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)

    Google Scholar 

  • Chen SQ, Li N, Guan J, Xie Y, Sun F, Ni J (2008). A statistical method to investigate national energy consumption in the residential building sector of China. Energy and Buildings, 40: 654–665.

    Article  Google Scholar 

  • Chen X, Zhang GQ, Yu LK (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.

    Google Scholar 

  • Chow TT, Au WH, Yau R, Cheng V, Chan, A, Fong KF (2004a). Applying district-cooling technology in Hong Kong. Applied Energy, 79: 275–289.

    Article  Google Scholar 

  • Chow TT, Chan ALS, Song CL (2004b). Building-mix optimization in district-cooling system implementation. Applied Energy, 77: 1–13.

    Article  Google Scholar 

  • Chow TT, Fong KF, Chan ALS, Yau R, Au WH, Cheng V (2004c). Energy modeling of district cooling system for new urban development. Energy and Buildings, 36: 1153–1162.

    Article  Google Scholar 

  • Corrado G (1921). Measurement of Inequality of Incomes. The Economic Journal, 31: 124–126.

    Article  Google Scholar 

  • D’Oca S, Hong T (2015). Occupancy schedules learning process through a data mining framework. Energy and Buildings, 88: 395–408.

    Article  Google Scholar 

  • de Dear RJ, Brager GS (2002). Thermal comfort in naturally ventilated buildings: Revision to ASHRAE standard 55. Energy and Buildings, 34: 549–561.

    Article  Google Scholar 

  • Devotta S, Padalkar AS, Sane NK (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.

    Article  Google Scholar 

  • Fanger PO (1970). Thermal Comfort: Analysis and Applications in Environmental Engineering. New York: McGraw-Hill.

    Google Scholar 

  • Feng X, Yan D, Hong T (2015). Simulation of occupancy in buildings. Energy and Buildings, 87: 348–359.

    Article  Google Scholar 

  • Frontczak M, Wargocki P (2011). Literature survey on how different factors influence human comfort in indoor environments. Building and Environment, 46: 922–937.

    Article  Google Scholar 

  • Hoes P, Hensen JLM, Loomans MGLC, de Vries B, Bourgeois D (2009). User behavior in whole building simulation. Energy and Buildings, 41: 295–302.

    Article  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.

    Google Scholar 

  • Jing ZX, Jiang XS, Wu QH, Tang WH, Hua B (2014). Modelling and optimal operation of a small-scale integrated energy based district heating and cooling system. Energy, 73: 399–415.

    Article  Google Scholar 

  • Jones B (2000). Capabilities and limitations of thermal models, Smart Controls and Thermal Comfort Project. Final Report (Confidential). Oxford Brookes University, pp. 112–121.

    Google Scholar 

  • Kamal WA (1997). Improving energy efficiency—The cost-effective way to mitigate global warming. Energy Conversion and Management, 38: 39–59.

    Article  Google Scholar 

  • Korolija I, Marjanovic-Halburd L, Zhang Y, Hanby VI (2011). Influence of building parameters and HVAC systems coupling on building energy performance. Energy and Buildings, 43: 1247–1253.

    Article  Google Scholar 

  • Lam JC, Wan KKW, Tsang CL, Yang L (2008). Building energy efficiency in different climates. Energy Conversion and Management, 49: 2354–2366.

    Article  Google Scholar 

  • Lenzena M, Wier, M, Cohen C, Hayami H, Pachauri S, Schaeffer R (2006). A comparative multivariate analysis of household energy requirements in Australia, Brazil, Denmark, India and Japan. Energy, 31: 181–207.

    Article  Google Scholar 

  • Long WD, Zhong T, Zhang BH (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.

    Google Scholar 

  • Meyers S, McMahon JE, McNeil M, Liu X (2003). Impacts of US federal energy efficiency standards for residential appliances. Energy, 28: 755–767.

    Article  Google Scholar 

  • Morelli N (2001). Technical innovation and resource efficiency. A model for Australian household appliances. The Journal of Sustainable Product Design, 1: 3–17.

    Article  Google Scholar 

  • Nagota T, Shimoda Y, Mizuno M (2008). Verification of the energysaving effect of the district heating and cooling system—Simulation of an electric-driven heat pump. Building and Environment, 40: 732–741.

    Article  Google Scholar 

  • Nicol JF (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.

    Google Scholar 

  • O’Doherty J, Lyons S, Tol RSJ (2008). Energy-using appliances and energy-saving features: Determinants of ownership in Ireland. Applied Energy, 85: 650–662.

    Article  Google Scholar 

  • Ortiga J, Bruno JC, Coronas A (2013). Operational optimisation of a complex trigeneration system connected to a district heating and cooling network. Applied Thermal Engineering, 50: 1536–1542.

    Article  Google Scholar 

  • Page J, Robinson D, Morel N, Scartezzini J-L (2008). A generalised stochastic model for the simulation of occupant presence. Energy and Buildings, 40, 2: 83–98.

    Article  Google Scholar 

  • Pardo N, Vatopoulos K, Riekkola AK, Perez A (2013). Methodology to estimate the energy flows of the European Union heating and cooling market. Energy, 52: 339–352.

    Article  Google Scholar 

  • Peeters L, de Dear R, Hensen J, D’haeseleer W (2009). Thermal comfort in residential buildings: Comfort values and scales for building energy simulation. Applied Energy, 86: 772–780.

    Article  Google Scholar 

  • Peng C, Yan D, Wu R, Wang C, Zhou X, Jiang Y (2012). Quantitative description and simulation of human behavior in residential buildings. Building Simulation, 5: 85–94.

    Article  Google Scholar 

  • Pérez-Lombard L, Ortiz J, Pout C (2008). A review on buildings energy consumption information. Energy and Buildings, 40: 394–398.

    Article  Google Scholar 

  • Reinhart CF (2004). Lightswitch-2002: A model for manual and automated control of electric lighting and blinds. Solar Energy, 77: 15–28.

    Article  Google Scholar 

  • Ren X, Da Y, Wang C (2014). Air-conditioning usage conditional probability model for residential buildings. Building and Environment, 81: 172–182.

    Article  Google Scholar 

  • Rezaie B, Rosen MA (2012). District heating and cooling: Review of technology and potential enhancements. Applied Energy, 93: 2–10.

    Article  Google Scholar 

  • Richardson I, Thomson M, Infield D (2008). A high-resolution domestic building occupancy model for energy demand simulations. Energy and Buildings, 40, 8: 1560–1566.

    Article  Google Scholar 

  • Rijal HB, Tuohy P, Humphreys MA, Nicol JF, Samuel A, Clarke J (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.

    Article  Google Scholar 

  • Shimoda Y, Nagota T, Isayama N, Mizuno M (2008). Verification of energy efficiency of district heating and cooling system by simulation considering design and operation parameters. Building and Environment, 43: 569–577.

    Article  Google Scholar 

  • Soederman J (2007). Optimisation of structure and operation of district cooling networks in urban regions. Applied Thermal Engineering, 27: 2665–2676.

    Article  Google Scholar 

  • Tanimoto J, Hagishima A, Sagara H (2008). A methodology for peak energy requirement considering actual variation of occupants’ behavior schedules. Building and Environment, 43: 610–619.

    Article  Google Scholar 

  • Ueno T, Sano F, Saeki O, Tsuji K (2006). Effectiveness of an energyconsumption information system on energy savings in residential houses based on monitored data. Applied Energy, 83: 166–183.

    Article  Google Scholar 

  • Wang C, Yan D, Jiang Y (2011). A novel approach for building occupancy simulation. Building Simulation, 4:149–167.

    Article  Google Scholar 

  • Wei S, Jones R, de Wilde P (2014). Driving factors for occupantcontrolled space heating in residential buildings. Energy and Buildings, 70: 36–44.

    Article  Google Scholar 

  • Weibull W (1951). A statistical distribution function of wide applicability. ASME Journal of Applied Mechanics, 18, 3: 293–297.

    MATH  Google Scholar 

  • Yan D, Xia J, Tang W, Fangting Song F, Zhang X, Jiang Y (2008). DeST—An integrated building simulation toolkit, Part I: Fundamentals. Building Simulation, 1: 95–110.

    Article  Google Scholar 

  • Yan D, O’Brien W, Hong T, Feng X, Gunay HB, Tahmasebi F, Mahdavi A (2015). Occupant behavior modeling for building performance simulation: Current state and future challenges. Energy and Buildings, 107: 264–278.

    Article  Google Scholar 

  • Yoshino H, Yoshino Y, Zhang Q, Mochida A, Li N, Li Z, Miyasaka H (2006). Indoor thermal environment and energy saving for urban residential buildings in China. Energy and Buildings, 38: 1308–1319.

    Article  Google Scholar 

  • Zhang X, Xia J, Jiang Z, Huang J, Qin R, Zhang Y, Liu Y, Jiang Y (2008). DeST—An integrated building simulation toolkit, Part I: Applications. Building Simulation, 1: 193–209.

    Article  Google Scholar 

  • Zhang WJ, Huai LL, Liu JL (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.

    Google Scholar 

  • Zhao J, Lasternas B, Lam KP, Yun R, Loftness V (2014). Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining. Energy and Buildings, 82: 341–355.

    Article  Google Scholar 

  • Zhou X, Hong T, Yan D (2013). Comparison of building energy modeling programs: HVAC systems. Available at http://www.osti.gov/ scitech/biblio/1165199. Accessed May 2015.

    Google Scholar 

  • Zhou X, Hong T, Yan D (2014). Comparison of HVAC system modelling in EnergyPlus, DeST and DOE-2.1E. Building Simulation, 7: 21–33.

    Article  Google Scholar 

  • Zhu D, Yan D, Wang C, Hong T (2013). A detailed loads comparison of three building energy modeling programs: EnergyPlus, DeST and DOE-2.1E. Building Simulation, 6: 323–335.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Architecture, Southeast University, Nanjing, 210096, China

    Xin Zhou

  2. School of Architecture, Tsinghua University, Beijing, 100084, China

    Da Yan, Xiaohang Feng & Yi Jiang

  3. Shanghai Research Institute of Building Sciences Group, Shanghai, 610041, China

    Guangwei Deng

  4. Beijing University of Technology, Beijing, 100084, China

    Yiwen Jian

Authors
  1. Xin Zhou
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  2. Da Yan
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  3. Xiaohang Feng
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  4. Guangwei Deng
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  5. Yiwen Jian
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  6. Yi Jiang
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Correspondence to Xin Zhou.

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Zhou, X., Yan, D., Feng, X. et al. Influence of household air-conditioning use modes on the energy performance of residential district cooling systems. Build. Simul. 9, 429–441 (2016). https://doi.org/10.1007/s12273-016-0280-9

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  • DOI: https://doi.org/10.1007/s12273-016-0280-9

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