n the present paper, the control design of Heating, Ventilation and Air Conditioning (HVAC) systems is investigated. In large-scale buildings – e.g. hotels or hospitals – the high dimension of the control design problem precludes a solution with reasonable computational effort. In this paper, a distributed control strategy is proposed, where interacting agents are operating sub-systems; interaction between these agents can ensure that an optimum solution can be obtained. A novel method to distributed control is introduced based on data-driven modeling where the strategy is not based on explicit optimization, but on weighted learning of the control rules; two examples of the addressed system are formulated. A significant advantage of the proposed approach consists in minimal assumptions on the addressed system and the most significant disadvantage is the need of sufficiently rich data-sets.