Reducing building energy consumption while ensuring indoor comfort conditions is driving the building envelope transition from static to responsive. In this context, electrochromic windows represent a promising solution to manage optical and thermal requirements in response to changing boundary conditions or user requirements, and control strategies play a central role in the exploitation of this technology. The present paper proposes a Hybrid Model Predictive Control strategy for the online operation optimization of an electrochromic-based active fa��ade. The controller was implemented and tested on a simulated case study. This was achieved by co-simulating the optimal controller which used reduced data-driven grey-box model, with a high accuracy white-box physical model. This setup enabled to close the control loop and to perform an accurate performance benchmarking. Results showed that the proposed Hybrid Model Predictive Control strategy outperformed two baseline Rule Based Controllers, reducing simultaneously energy consumption, peak power and percentage of discomfort hours by up to 82%, 71% and 51% respectively. In comparison with the two baseline controllers and considering all the scenarios, the overall energy consumption could be reduced by approximately 40%���60% on average. In addition the work demonstrates how tuning the Hybrid Model Predictive Control weights adds flexibility to the controller, significantly changing its behaviour to meet the requirements of the designer.