Ocean waves represent a significant energy resource which can complement other renewable energy technologies during the transition to a low-carbon energy mix. Despite the large number of concepts suggested for the conversion of wave energy, none of the technologies has yet demonstrated economic viability. To this end, several solutions have been proposed in the literature, such as deploying Wave Energy Converters (WECs) in large arrays or optimal control of WECs. The majority of WEC optimal control strategies require knowledge of the previous, current, and future excitation force acting on the device. However, for the WEC case, the excitation force is an unmeasurable quantity and, therefore, must first be estimated, based on available measurements, and then predicted in the future. The main objective of this thesis is to analyse the estimation/prediction techniques proposed for wave energy applications and to evaluate whether such techniques are ready to be applied for real WEC control strategies. To this end, a critical comparison of the available excitation force estimators is presented. Additionally, the performance of the autoregressive model as a predictor is analysed, showing that, the obtained prediction accuracy can get close to the theoretically best achievable prediction accuracy. Based on the errors observed from the analysis of excitation force estimation/prediction techniques, a sensitivity analysis of an optimal control strategy to such errors is performed. As a result, this thesis provides an overview of the aspects which should be considered at the stage of tuning estimation/prediction techniques, to not affect the controller performance. Since the estimation/prediction problem becomes more challenging for WEC arrays, due to the hydrodynamic interactions, an important question is whether the extra measurements from the array are sufficient to compensate for the greater complexity of the wave field. Thus, a global estimator/predictor, considering information from all the devices of the array, is developed and compared to a set of independent estimators/predictors. Finally, this thesis introduces an identification strategy to obtain a parametric model of both the force-to-motion dynamics and/or the radiation force convolution term of the device. The strategy allows for the identification of low-order parametric models of WECs, which will simplify the implementation of optimal control strategies in real-time. Additionally, the proposed strategy is compared to the other approaches available in the literature.