Wind turbine generators (WTGs) are one of the most widely used sources of renewable energy currently available. To accurately predict their production and quickly notice any anomalies, it is important to analyze the data produced by these turbines to understand their behavior and patterns. The purpose of this thesis is to create a data-driven digital twin of a wind turbine generator capable of simulating its ideal behavior. To carry out this task, the model receives input data of environmental variables, including wind speed and ambient temperature, and produces output values of parameters that a turbine should have under ideal conditions, including produced power, rotor speed, and more.