In the last years, Bayesian optimization (BO) has emerged as a practical tool for high-quality parameter selection in prediction systems. BO methods are useful for optimizing black-box objective functions that either lack an analytical expression, or are very expensive to evaluate. In this paper we show how BO can be used to obtain optimal parameters of a prediction system for a problem of wave energy flux prediction. Specifically, we propose the Bayesian optimization of a hybrid Grouping Genetic Algorithm with an Extreme Learning Machine (GGA-ELM) approach. The system uses data from neighbor stations (usually buoys) in order to predict the wave energy at a goal marine energy facility. The proposed BO methodology has been tested in a real problem involving buoys data in the Western coast of the USA, improving the performance of the GGA-ELM without a BO approach.