A considerable amount of literature on the application of inverse methods to building energy data has been published in the last three decades. These inverse models serve a variety of purpose such as baseline modeling for monitoring and verification (M&V) projects at monthly, daily, and hourly time scales; condition monitoring; fault detection and diagnosis; supervisory control; and load forecasting, to name a few. Usually, these models have distinct model structures, and separate models are developed depending on the specified need. This paper proposes a novel inverse modeling framework that attempts to unify some of the above application-specific models by allowing a single model to be incrementally enhanced. Specifically, we begin by clustering the energy interval data of a building, identifying scheduling day types and removing any outliers. This aspect of the analysis is described in the companion paper by Jalori and Reddy (2015). Subsequently, the first level is to identify models for each day type using daily average values of energy use and climatic variables; this is adequate in many M&V projects. These models are then extended to hourly time scales by including additional terms in the model that capture diurnal variations of the climatic variables and the building hourly scheduling about the daily mean value; this level is appropriate for M & V and for condition monitoring. Finally, periodic autoregressive terms are added to the model to enhance prediction accuracy for short-term load forecasting, useful for demand response programs, or for short-term supervisory control. The application of the proposed methodology is illustrated with year-long data from two different buildings, one synthetic (the Department of Energy medium-office prototype) building and an actual office building.