Free cooling by enhanced night-time ventilation could be an efficient technique for decreasing the energy demand for the cooling of buildings. Such systems use the ambient cold of surrounding air, which is transported into buildings by mechanical fan-driven ventilation systems. Only carefully designed and operated systems can be efficient enough to compete with other free-cooling techniques, such as evaporative cooling or even compressor-driven mechanical cooling systems. The efficiency of free cooling by enhanced night-time ventilation could be significantly improved if a model-based predictive weather control algorithm is used for operation control. The aim of this article is to present a generalized model-based predictive weather control (G-MPWC) algorithm that was developed with a detailed short time step numerical simulation of the thermal response of the building and free cooling system and simplified weather forecast data. The result of the G-MWPC is a set of control matrixes that includes data on the forecast required night-time air exchange rate and forecast daily coefficient of the performance of the free cooling system regarding the forecast daily average ambient air temperature and amplitude and the pre-set free cooling system on/off temperature difference. An additional matrix that includes data on forecast maximum indoor air temperature is developed for the case of the free cooling system being unable to fulfil pre-set thermal comfort requirements. A unique set of G-MWPS control matrixes must be developed for specific buildings and building operation conditions; afterwards, that control matrix can be used for the predicted controlling of the free cooling system by night time ventilation for the whole range of summer time meteorological conditions. In this article, the method and numerical model for developing a G-MPWC algorithm is presented as well as an example of a control matrix developed for a typical office room. The G-MPWC algorithm was validated for the case of a free-cooled office room. (C) 2016 Elsevier Ltd. All rights reserved.