Ocean waves represent an important resource of renewable energy, which can provide a significant support to the development of more sustainable energy solutions and to the reduction ofCO2 emissions. The amount of extracted energy from the ocean waves can be increased by optimizing the geometry and the control strategy of the wave energy converter (WEC), which both require mathematical hydrodynamic models, able to correctly describe the WEC-fluid interaction. In general, the construction of a model is based on physical laws describing the system under investigation. The hydrodynamic laws are the foundation for a complete description of the WEC-fluid interaction, but their solution represents a very complex and challenging problem. Different approaches to hydrodynamic WEC-fluid interaction modelling, such as computational fluid dynamics (CFD) and linear potential theory (LPT), lead to different mathematical models, each one characterised by different accuracy and computational speed. Fully nonlinear CFD models are able to describe the full range of hydrodynamic effects, but are very computationally expensive. On the other hand, LPT is based on the strong assumptions of inviscid fluid, irrotational flow, small waves and small body motion, which completely remove the hydrodynamic nonlinearity of the WEC-fluid interaction. Linear models have good computational speed, but are not able to properly describe nonlinear hydrodynamic effects, which are relevant in some WEC power production conditions, since WECs are designed to operate over a wide range of wave amplitudes, experience large motions, and generate viscous drag and vortex shedding. The main objective of this thesis is to propose and investigate an alternative pragmatic framework, for hydrodynamic model construction, based on system identification methodologies. The goal is to obtain models which are between the CFD and LPT extremes, a good compromise able to describe the most important nonlinearities of the physical system, without requiring excessively computational time. The identified models remain sufficiently fast and simple to run in real-time. System identification techniques can ‘inject’ into the model only the information contained in the identification data; therefore, the models obtained from LPT data are not able to describe nonlinear hydrodynamic effects. In this thesis, instead of traditional LPT data, experimental wave tank data (both numerical wave tank (NWT), implemented with a CFD software package, and real wave tank (RWT)) are proposed for hydrodynamic model identification, since CFD-NWT and RWT data can contain the full range of nonlinear hydrodynamic effects. In this thesis, different typologies of wave tank experiments and excitation signals are investigated in order to generate informative data and reduce the experiment duration. Indeed, the reduction of the experiment duration represents an important advantage since, in the case of a CFD-NWT, the amount of computation time can become unsustainable whereas, in the case of a RWT, a set of long tank experiments corresponds to an increase of the facility renting costs.