Structure-preserving model reduction of nonlinear building thermal models

This paper proposes an aggregation-based model reduction method for nonlinear models of multi-zone building thermal dynamics. The full-order model, which is already a lumped-parameter approximation, quickly grows in state space dimension as the number of zones increases. An advantage of the proposed method, apart from being applicable to the nonlinear thermal models, is that the reduced model obtained has the same structure and physical intuition as the original model. This makes the reduced model useful not only for control design and analysis but also for building design iterations. The key to the methodology is an analogy between a continuous-time Markov chain and the linear part of the thermal dynamics. A recently developed aggregation-based method of Markov chains is employed to aggregate the large state space of the full-order model into a smaller one. Simulations are provided to illustrate tradeoffs between prediction error and computation time.