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

[Display omitted] •This study analyses pre- and post-retrofit energy performance data for 56 office buildings in Singapore.•The buildings are classified into clusters using k-means clustering algorithm.•A robust methodology is developed to determine the appropriate variables to be used for clustering.•The clustered buildings are evaluated to study the change in energy performance between pre- and post-retrofit conditions. 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/m2.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.