This paper presents a clustering strategy to evaluate the energy performance and identify typical daily load profiles of buildings. The cluster analysis included intra-building clustering and inter-building clustering. The intra-building clustering used Gaussian mixture model clustering to identify the typical daily load profiles of each individual building. The inter-building clustering used hierarchical clustering to further identify the typical daily load profiles of a stock of buildings based on the typical daily load profiles identified for each individual building. The performance of this strategy was tested and evaluated using the two-year hourly electricity consumption data collected from 40 buildings on a university campus in Australia. The result showed that this strategy could discover the information related to building energy usage. The results obtained from this study could be potentially used to assist in decision making for energy performance enhancement initiatives of university buildings.