The class of buildings designated as commercial is comprised of many different architectures and functions, which presents a challenge when developing demand management strategies that are applicable across this field. Most advanced controllers are based on a model of the thermal loads in the building envelope. These models do not directly extrapolate to measures of power consumption, which is what building owners are ultimately interested in managing. In this work, we develop models of power use that can be easily tailored to model power demand in any commercial building with advanced sensing and metering. We address how existing models of the building temperature states can be incorporated into our framework. Specifically, we show how Auto-Regressive models with eXogeneous (ARX) inputs can be used for superior day-ahead forecasting of demand, and how to formulate the models in a way that is meaningful at the supervisory level. These models provide more flexibility in the design of a supervisory controller for building energy management and the information they provide is crucial for bridging the gap between buildings and utilities in the smart grid.

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