State-space models for building control: how deep should you go?

Abstract

Power consumption in buildings show nonlinear behaviours that linear models cannot capture, whereas recurrent neural networks (RNNs) can. This ability makes RNNs attractive alternatives for the model-predictive control (MPC) of buildings. However, RNNs are nonlinear and non-smooth functions which makes their use challenging in optimization problems. Therefore, this work systematically investigates whether using RNNs for building control provides net gains in MPC. It compares over 2 months of simulated operation the representation power and control performance of two architectures: an RNN architecture and a linear state-space (LSS) model with a nonlinear regressor to estimate energy consumption. The results show that RNNs yield an identification error 69% lower than LSS, but the LSS models yield control laws that achieve 10% lower objective function with a computational time three times lower than the RNNs. Thus, on balance, well-designed LSS models with nonlinear regressors are best in most cases of MPC.