Short-term forecasting of the wave energy flux: Analogues, random forests, and physics-based models

• Short-term forecasting of wave energy flux has been carried out using random forests (RF).
• The best results are obtained in near-shore locations.
• In these locations, random forests outperform both a physics-based model and a persistence forecast between 3 and 16–19 h ahead.

This paper analyzes the performance of three types of statistical models and a well-known physics-based model for forecasting the wave energy flux. The forecasts are run over horizons of 1–24 h at five buoys located in the Bay of Biscay. The data resolution is hourly. The 1999–2005 timeframe is used to train the models. The forecasts are run and evaluated over the six-year period from 2006 to 2012. The statistical forecasting models use three techniques: analogues, random forests (a machine learning algorithm) and a combination of the two. The physics model is the Wave Model (WAM). The forecasts are compared at a 95% confidence level with the simplest prediction – Persistence – and also with the nearest grid point of the WAM forecasts. Over horizons between 3 and 16–19 h at locations near the coast (where wave farms may be installed), the random forests models outperform the others, including WAM and Persistence. These models exploit the inherent predictability associated with the strong autocorrelation present in ocean energy values. The additional prognostic capabilities that random forests models provide over Persistence, are due to their ability to sucessfully incorporate the information that both, atmospheric and sea-state variables provide.