Short-term prediction in an Oscillating Water Column using Artificial Neural Networks

The oscillating-water-column (OWC) is widely regarded as the simplest and most reliable type of wave energy converter. An OWC device comprises a partly submerged hollow structure composed of an aperture below the water surface and an air chamber above the free surface. The oscillating motion of the internal free water surface produced by the incident waves compresses and expands the air in the chamber. The pressure between the air chamber and the atmosphere can be used to drive a turbine connected directly to an electrical generator. Usually the turbines used in OWCs are of the self-rectifying type, i.e., they rotate always in the same sense independently of the flow direction. The aim of this paper is to assess the potential use of experimental data collected in the Mutriku breakwater wave power plant, located in the north of Spain, to perform short-term prediction of the pressure inside the air chamber by a Nonlinear Autoregressive with eXogenous inputs (NARX) Artificial Neural Network (ANN). The NARX model is explored and tuned for a one-step and multi-step-ahead prediction. The results showed that the model is able to capture the OWC system dynamics for a few-steps ahead with a good performance.