A hybrid simulation approach to predict cooling energy demand for public housing in Hong Kong

The residential sector accounts for a significant and increasing portion of the national energy use. Cooling energy reduction in the public housing sector is one of the key success factors for sustainable building development measures especially in the sub-tropics. This study proposes a hybrid EnergyPlus (EP) — artificial neural network (ANN) model, which is more flexible and time-efficient than the conventional cooling energy simulation methods, for simulating the cooling energy consumption in the Hong Kong public housing sector and evaluates the cooling energy impacts related to building materials, window sizes, indoor-outdoor temperature variations and apartment sizes. The results show that climate changes and temperature set-points have the greatest impact on cooling energy use (−19.9% to 24.1%), followed by flat size combinations (−13.4% to 27.9%). The proposed model can be a useful tool for policymakers to establish sustainable public housing development plans in Hong Kong.

[1]  Soteris A. Kalogirou,et al.  Artificial neural networks in energy applications in buildings , 2006 .

[2]  Xunmin Ou,et al.  Life-cycle energy consumption and greenhouse gas emissions for electricity generation and supply in China , 2011 .

[3]  Jin Wen,et al.  Review of building energy modeling for control and operation , 2014 .

[4]  Joseph C. Lam,et al.  Residential sector air conditioning loads and electricity use in Hong Kong , 2000 .

[5]  Nelson Fumo,et al.  A review on the basics of building energy estimation , 2014 .

[6]  Mark Luther,et al.  Energy efficient envelope design for high-rise apartments , 2005 .

[7]  Daniel E. Fisher,et al.  EnergyPlus: creating a new-generation building energy simulation program , 2001 .

[8]  Gerd Hauser,et al.  Passive Building Design: A Handbook of Natural Climatic Control , 1994 .

[9]  L. T. Wong,et al.  An occupant load survey for residential buildings in Hong Kong , 2006 .

[10]  Joseph Virgone,et al.  Development and validation of regression models to predict monthly heating demand for residential buildings , 2008 .

[11]  V. Ismet Ugursal,et al.  Modeling of end-use energy consumption in the residential sector: A review of modeling techniques , 2009 .

[12]  Wil L. Kling,et al.  Pseudo Dynamic Transitional Modeling of Building Heating Energy Demand Using Artificial Neural Network , 2014, ArXiv.

[13]  K. Mui,et al.  Energy impact of indoor environmental policy for air-conditioned offices of Hong Kong , 2008 .

[14]  Ya Feng,et al.  Thermal design standards for energy efficiency of residential buildings in hot summer/cold winter zones , 2004 .

[15]  S. H. Lek.  Energy Efficiency for Tropical Campus , 2006 .

[16]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[17]  John Burnett,et al.  Influence of envelope and partition characteristics on the space cooling of high-rise residential buildings in Hong Kong , 2002 .

[18]  K.S.Y. Wan,et al.  Representative building design and internal load patterns for modelling energy use in residential buildings in Hong Kong , 2004 .

[19]  K. W. Mui,et al.  Cooling load calculations in subtropical climate , 2007 .

[20]  L. T. Wong,et al.  Electricity energy trends in Hong Kong residential housing environment , 2014 .

[21]  Danny H.W. Li,et al.  Impact of climate change on energy use in the built environment in different climate zones – A review , 2012 .

[22]  Shiming Deng,et al.  The outdoor air ventilation rate in high-rise residences employing room air conditioners , 2003 .