Good practice in smart grids is networked sensors topologies which give the ability to fuse sensors to obtain a new feature based on sensors’ complementarity. Moreover, to get a prediction based on other sensors, possibly in other areas, in case of sensor failure. This ability may be achieved by correlating sensor data based on their historical observations. This study addresses the problem of stochastic multivariate modelling within the framework of cyber-physical systems. Weibull distribution function (WDF) was found to be the best probability distribution function to fit wind variations. The main idea of this study is to propose a generalised multivariate stochastic model to describe the joint probability between multi-stochastic variables. A novel multivariate WDF for wind modelling is proposed to estimate wind speed in one location based on wind speed data on other location(s). The suggested approach is applied to three cases to demonstrate the efficacy of the developed technique. It is pointed out that this approach could be generalised for the multivariate WDF of wind modelling situations. Finally, it is shown that there are existing structures resemblances between the wind Weibull versus normal distribution models arising from the fact WDF reflects a natural phenomenon with some restrictions.