Wave energy has great potential as a renewable source of electricity. Installed capacity is increasing, and developments in technology mean that wave energy is likely to play an important role in the future mix of electricity generation. Short-term forecasts of wave energy are required for the efficient operation of wave farms and power grids, as well as for energy trading. The intermittent nature of wave energy motivates the use of probabilistic forecasting. In this paper, we evaluate the accuracy of probabilistic forecasts of wave energy flux from a variety of methods, including unconditional and conditional kernel density estimation, univariate and bivariate autoregressive moving average generalised autoregressive conditional heteroskedasticity (ARMA-GARCH) models, and a regression-based method. The bivariate ARMA-GARCH models are implemented with different pairs of variables, such as (1) wave height and wave period, and (2) wave energy flux and wind speed. Our empirical analysis uses hourly data from the FINO1 research platform in the North Sea to evaluate density and point forecasts, up to 24 h ahead, for the wave energy flux. The empirical study indicates that a bivariate ARMA-GARCH model for wave height and wave period led to the greatest accuracy overall for wave energy flux density forecasting, but its usefulness for point forecasting decreases as the lead time increases. The model also performed well for wave power data that had been generated from wave height and wave period observations using a conversion matrix.
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