Extending Wave in situ Measurements Through Gaussian Process Regression: An Experimental Campaign

Wave energy is recognized as one of the most promising sources of clean and abundant energy. Nevertheless, as of today this technology is still not commercially viable due to a number of reasons, such as the harshness of the sea environment, the expenses needed for the deployment and maintenance of devices in open ocean, and the lack of information regarding wave parameters worldwide. Indeed, a proper characterization of the resource in a site is of quintessential importance for assessing the productivity of the site and dimensioning the supporting system of a device. This work aims to address the problem of the lack of data by resorting to spatial prediction techniques, using data gathered through an experimental campaign conducted at the wave basin facility at the Ocean and Coastal Engineering Laboratory in Aalborg University. During this campaign, two months of data from a real in situ measuring device were replicated in the basin. In the middle of the basin, some concrete blocks were deployed underwater to replicate a sudden shift in the bathymetry, which should act as a disturbance to the wave propagation and arise nonlinear phenomena. Nineteen wave gauges recorded the wave elevation for the whole time. A scenario where only some of the measuring devices were working was replicated by considering only the data from a subsample of wave gauges and inferring the parameters in the locations of the other devices from them, through a Gaussian Process Regression (GPR) algorithm. The proposed algorithm was able to interpolate the parameters at the other locations, at the expense of a relatively low error, indicating that this set up could be used to increase the spatial coverage of the wave-measuring buoys deployed worldwide or to provide an estimate of the parameters at a buoy that is not working, e.g., for maintenance operations.