Modeling of HVAC components and energy flows for energy prediction purposes can be computationally expensive in large commercial buildings. More recently, the increased availability of building operational data has made it possible to develop data-driven methods for predicting and reducing energy use for these buildings. In this paper, we present such an approach, where we combine unsupervised and supervised learning algorithms to develop a robust method for energy reduction for large buildings operating under different environmental conditions.