Design matters: New insights on optimizing energy consumption for residential buildings
Artikel i vetenskaplig tidskrift, 2021

In this paper, we construct a unique database for 1228 residential buildings in Hong Kong to investigate how the spatial features of these residential buildings affect the electricity consumption in the communal area. We choose Hong Kong for this analysis as the city owns a large number of standard-type residential buildings managed by the public institution, which could be affected strongly by environmental policies. Both the machine learning method, based on the Least Absolute Shrinkage and Selection Operator (LASSO), and econometric regressions are adopted to analyse the data. We first utilize the machine learning LASSO technique to identify the most relevant factors for the subsequent econometric analysis. Our results show that the electricity demand for relatively low consumption building types, such as Twin Tower, is 6% lower than that of the high consumption building types. Newly constructed buildings usually belong to the medium consumption types, with the estimated monthly electricity consumption per apartment in communal areas to be around 50.2 kWh on average in 2020. These findings shed light on the nexus between spatial features and energy use for complex buildings, potentially contributing to the better crafting of energy-saving policy and the improvement of residential building programmes.

Energy consumption

Building type

Machine learning

LASSO method

Building shape

Spatial feature


Weili Sheng

City University of Hong Kong

Xiaoming Kan

Chalmers, Rymd-, geo- och miljövetenskap, Fysisk resursteori

Bo Wen

City University of Hong Kong

Lin Zhang

City University of Hong Kong

Energy and Buildings

0378-7788 (ISSN)

Vol. 242 110976



Miljöanalys och bygginformationsteknik




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