State-space models for building control: how deep should you go?
Rights accessOpen Access
European Commission's projectSABINA - SmArt BI-directional multi eNergy gAteway (EC-H2020-731211)
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.
CitationSchubnel, B. [et al.]. State-space models for building control: how deep should you go? "Journal of Building Performance Simulation", 14 Setembre 2020, vol. 13, núm. 6, p. 707-719.