In the U.S., commercial and residential buildings and their occupants consume more than 40% of total energy and are responsible for 45% of total greenhouse gas (GHG) emissions. Therefore, saving energy and costs, improving energy efficiency and reducing GHG emissions are key initiatives in many cities and municipalities and for building owners and operators. To reduce energy consumption in buildings, one needs to understand patterns of energy usage and heat transfer as well as characteristics of building structures, operations and occupant behaviors that influence energy consumption. We develop heat transfer inverse models and statistical models that describe how energy is consumed in commercial buildings, and simulate the impact of energy saving changes that can be made to commercial buildings including structural, operational, behavioral and weather changes, on energy consumption and GHG emissions. The analytic toolset identifies energy savings opportunities and quantifies the savings for a large portfolio of public buildings. © 2011 IEEE.