On the spatial interpolation of ocean energy source variables: A comparative analysis

In the context of wave energy systems development, the estimation of wave parameters, such as the energy period (Te), over the entire ocean surface is of paramount importance. These information are crucial for estimating the energy harvesting potential of deployment sites, designing wave energy converters (WECs), and planning optimal maintenance intervention frequency. However, measuring Te at every point in the ocean is impossible due to the vastness of the ocean and due to the cost and difficulties of installing and maintaining wave instrumentation buoys, since these have to survive in marine environment. As a consequence, the amount of data available is too limited and sparse in space, making it impractical to perform these analyses with precision. To address such data scarcity and sparsity, we analyse in this paper various spatial interpolation techniques employed to fill the spatial gaps in the wave parameter datasets. Three types of interpolators are considered: linear interpolator, spline interpolator, and radial basis functions (RBFs) interpolator. These algorithms are trained and tested on a public dataset of wave parameters from Copernicus Marine Service in an area between the coastlines of South England and North France. To simulate the available data scarcity and sparsity, only limited percentages of the ocean area are considered covered and available in the training stage (from 1% to 5%). The performance of each interpolator is evaluated in terms of Normalized Root Mean Square Error (NRMSE) achieved by the algorithm in reconstructing the parameters at the unsampled locations. The results of this study demonstrate the feasibility of spatial gapfilling of wave parameter, and demonstrates that the RBF algorithm outperforms the other two algorithms in terms of robustness to different training points sampling, when working with low training percentages.