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Representative building design and internal load patterns for modelling energy use in residential buildings in Hong Kong

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  • Wan, K. S. Y.
  • Yik, F. H. W.

Abstract

Based on the data collected in recent surveys, models have been established to represent the energy characteristics of living and dining rooms and bedrooms in typical residential buildings in Hong Kong, in respect of the layout and construction; the density and pattern of occupation; the power intensities and operating patterns of lighting and appliances; and air-conditioner operation patterns. With the help of computer simulation, the impacts of varying these characteristics on the annual space cooling load have been evaluated, which allowed representative internal load patterns for residential units to be defined. These are essential data for predicting energy use in residential buildings, which, in turn, is an indispensable part of studies on ways to assess and minimise energy use in such buildings. This paper presents the building model and the internal load and air-conditioner operation patterns established in the study.

Suggested Citation

  • Wan, K. S. Y. & Yik, F. H. W., 2004. "Representative building design and internal load patterns for modelling energy use in residential buildings in Hong Kong," Applied Energy, Elsevier, vol. 77(1), pages 69-85, January.
    • Handle: RePEc:eee:appene:v:77:y:2004:i:1:p:69-85
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    Citations

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    Cited by:

    1. Li, Jun & Ng, S. Thomas & Skitmore, Martin, 2017. "Review of low-carbon refurbishment solutions for residential buildings with particular reference to multi-story buildings in Hong Kong," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 393-407.
    2. Attia, Shady & Evrard, Arnaud & Gratia, Elisabeth, 2012. "Development of benchmark models for the Egyptian residential buildings sector," Applied Energy, Elsevier, vol. 94(C), pages 270-284.
    3. Jia, Jie & Lee, W.L., 2015. "Experimental study of the application of intermittently operated SEHRAC (storage-enhanced heat recovery room air-conditioner) in residential buildings in Hong Kong," Energy, Elsevier, vol. 83(C), pages 628-637.
    4. Dongjun Suh & Seongju Chang, 2012. "An Energy and Water Resource Demand Estimation Model for Multi-Family Housing Complexes in Korea," Energies, MDPI, vol. 5(11), pages 1-20, November.
    5. Kwok Wai Mui & Ling Tim Wong & Manoj Kumar Satheesan & Anjana Balachandran, 2021. "A Hybrid Simulation Model to Predict the Cooling Energy Consumption for Residential Housing in Hong Kong," Energies, MDPI, vol. 14(16), pages 1-18, August.
    6. Gholami, M. & Torreggiani, D. & Tassinari, P. & Barbaresi, A., 2021. "Narrowing uncertainties in forecasting urban building energy demand through an optimal archetyping method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    7. Radhi, H., 2010. "On the optimal selection of wall cladding system to reduce direct and indirect CO2 emissions," Energy, Elsevier, vol. 35(3), pages 1412-1424.
    8. Chung, Mo & Park, Hwa-Choon, 2012. "Building energy demand patterns for department stores in Korea," Applied Energy, Elsevier, vol. 90(1), pages 241-249.
    9. Pukšec, Tomislav & Vad Mathiesen, Brian & Duić, Neven, 2013. "Potentials for energy savings and long term energy demand of Croatian households sector," Applied Energy, Elsevier, vol. 101(C), pages 15-25.
    10. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    11. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    12. Ma, Zhenjun & Wang, Shengwei, 2009. "Building energy research in Hong Kong: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1870-1883, October.
    13. Melo, A.P. & Cóstola, D. & Lamberts, R. & Hensen, J.L.M., 2014. "Development of surrogate models using artificial neural network for building shell energy labelling," Energy Policy, Elsevier, vol. 69(C), pages 457-466.
    14. Yik, F.W.H & Wan, K.S.Y, 2005. "An evaluation of the appropriateness of using overall thermal transfer value (OTTV) to regulate envelope energy performance of air-conditioned buildings," Energy, Elsevier, vol. 30(1), pages 41-71.

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