In this paper, our goal is to build a model for the future wind power generation of Alberta, as Alberta’s wind power capacity is growing, and new wind farms are expected to be built in the near future. An important feature of the wind power data is spatial and temporal correlation. To capture this, we model the wind power generation in Alberta as a spatio-temporal process. We apply the method of Gaussian random fields to analyze the wind power time series of 20 wind farms of Alberta. Following the work of Gneiting et al. [11] , we build several spatio-temporal covariance function estimates with increasing complexity: separable, non-separable symmetric, and non-symmetric. We compare the performance of the models using simple kriging. We also use kriging to demonstrate the performance of the models to forecast the future wind generation for both an existing wind farm and a new farm in Alberta. In the end, we also formulate the mean and variance of the aggregate wind power generation in Alberta
