Wind plant power output behaves like a function that strongly depends on the value and direction of wind speed, but the weather conditions have plenty of parameters that can affect the output. In this work, we are aimed to merge two different datasets to get the application for a wind turbine model. The first dataset contains weather parameters while the second consists of the wind power production for a region. Then we estimate the parameters of an equivalent wind turbine to evaluate physical model baseline. On the next step, we build and tune regression machine learning models: the gradient boosting with decision trees, Gaussian process, neural network and support vector machine. Additionally, we perform the sensitivity analysis for the most accurate trained models and get the most reliable weather parameters.
