The use of active thermal energy storage can provide energy savings, cost reduction and CO2 mitigation by reducing energy demand for heating and cooling, allowing the use of peak load shifting strategies and enhancing the introduction of renewable energies in the sector. The high investment cost of these systems makes mandatory an appropriate control in order to maximize the energy benefits during its operation. Within this context artificial intelligence techniques have been successfully used to control active thermal energy storage units. This paper uses an experimentally validated numerical tool to study the effect of different control strategies on the performance of one TES system applied to the building sector, a ventilated facade with PCM which uses free cooling. Three different strategies were designed to control the ventilated facade based on cost savings, energy reduction and CO2 mitigation, under different climatic conditions. Results show robust benefits in the three tested control strategies, achieving average savings in comparison to a manual operation of the system of 4.3%, 7.8%, and 16.7%. Moreover, the paper shows that when the control strategy is focused on optimizing cost, the other two benefits claimed by TES systems (energy and CO2 mitigation) are significantly reduced. (C) 2016 Elsevier B.V. All rights reserved.