Scenario-based nonlinear model predictive control for building heating systems

State-of-the-art Model Predictive Control (MPC) applications for building heating adopt either a deterministic controller together with nonlinear model or linearized stochastic MPC controller. However, only considers one single realization of the disturbances and its performance strongly depends on quality forecast disturbances, which can lead to low performance. In fact, inadequate energy management high costs CO$_2$ emissions. On other hand, fail capture some dynamics behavior under control. this article, we combine scenario-based (SBMPC) Modelica that is able provide richer description more accurately than linear models. The adopted SBMPC multiple realizations external obtained through statistically accurate model, so as consider different possible disturbance evolutions robustify control action. To purpose, present scenario generation method temperature be applied several exogenous perturbations, e.g.\ solar irradiance, outside temperature, satisfies important stastistical properties, in contrast simpler less methods literature. We show benefits our proposed approach simulations compare against standard ones from literature, combinations trade-off parameter between comfort cost. how outperforms controllers available