In this delivery, a control-focused hybrid model for wind turbines was developed by combining physics-based and data-driven components. The data-driven component addressed uncertainties and unmodeled dynamics, resulting in a hybrid model enhanced by machine learning. A framework was created to optimize the integration of both physics-based and data-driven parameters using operational data. To ensure computational efficiency, the model's complexity was reduced. The finalized hybrid model forms the basis for the model predictive controllers (MPCs) in tasks T3.2-3.4, allowing for a comprehensive comparison with purely physical models and data-driven models.