Wind turbine design, control strategies often assume Taylor’s frozen turbulence where the fluctuating part of the wind is assumed to be constant. In practise, the wind turbine faces higher turbulence in case of gusts and lower turbulence in some cases. With Lidar technology, the frozen turbulence assumption could be avoided and the evolution of wind towards the wind turbine could be studied. This studey therefore bridges the gap between measurements and controls of the turbine. In this poster, the autoregressive methods for prediction of the wind speeds evolving from farwind to nearwind are analysed and an empirical state space model is developed. The results are therefore useful in developing the transfer function for efficient wind turbine control thereby, reducing fatigue and extreme loads in the wind turbine.