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Commercial buildings are responsible for a large fraction of energyconsumption in developed countries, and therefore are targets of energyefficiency programs. Motivated by the large inherent thermal inertia ofbuildings, the power consumption can be flexibly scheduled without compromisingoccupant comfort. This temporal flexibility offers opportunities for theprovision of frequency regulation to support grid stability. To realize energysavings and frequency regulation, it is of prime importance to identify arealistic model for the temperature dynamics of a building. We identify a low-dimensional data-driven model and a high-dimensional physics- based model fordifferent spatial granularities and temporal seasons based on a case study ofan entire floor of Sutardja Dai Hall, an office building on the University ofCalifornia, Berkeley campus. A comparison of these contrasting models showsthat, despite the higher forecasting accuracy of the physics-based model, bothmodels perform almost equally well for energy efficient control. We concludethat the data-driven model is more amenable to controller design due to its lowcomplexity, and could serve as a substitution for highly complex physics- basedmodels with an insignificant loss of prediction accuracy for many applications.On the other hand, our physics-based approach is more suitable for modelingbuildings with finer spatial granularities.

Model Comparison of a Data-Driven and a Physical Model for Simulating HVAC Systems