Determining key variables influencing energy consumption in office buildings through cluster analysis of pre- and post-retrofit building data

作者：Deb Chirag^*; Lee Siew Eang

来源：Energy and Buildings, 2018, 159: 228-245.

DOI：10.1016/j.enbuild.2017.11.007

• Building energy
• Cluster analysis
• Energy efficiency
• Office buildings
• Building retrofit
• K-means clustering

This study aims to determine key building variables influencing energy consumption in air-conditioned office buildings. The study is based in Singapore which entails tropical climatic conditions. The analysis is based on assessment of several energy audit reports concerning pre- and post-retrofit data from 56 office buildings. A list of 14 building variables, extracted from these reports form the superset. These are systematically analyzed further to derive key variables influencing energy consumption and retrofitting decisions. For this purpose, a robust iterative process is developed utilizing k-means clustering. This process tests all combinations of the 14 variables against change in energy use intensity (EUI, measured as kWh/m(2).year) for pre- and post-retrofit conditions. The results indicate that the best set of variables consists of: 1) gross floor area (GFA), 2) non-air-conditioning energy consumption, 3) average chiller plant efficiency, and 4) installed capacity of chillers. This information can be utilized to explore energy saving potential of office buildings that need to be retrofitted. The resultant clusters can also be used to benchmark buildings based on pre-retrofit conditions and energy saving potential.

• 出版日期2018-1-15