Parameter estimation of a building system model and impact of estimation error on closed-loop performance

Predictive-control methods have been recently employed for demand-response control of building and district-level HVAC systems. Such approaches rely on models and parameter estimates to meet comfort constraints and to achieve the theoretical system-efficiency gains. In this paper we present a methodology that establishes achievable targets for control-model parameter estimation errors based on closed-loop performance sensitivity. The control algorithm is designed as a Model Predictive Controller (MPC) that uses perturbed building-model parameters. We perform simulations to estimate the dependency of energy cost and constraint infringement time on the magnitude of these perturbations. The simulation results are used to define targets for the parameter estimation errors, which in turn are applied to specify the character of excitation and model structure used for identification. We design a parameter estimator and perform Monte-Carlo simulations for a model that includes sensor noise and load uncertainty. The distribution of the estimation errors are used to demonstrate that the established targets are met.