DATA-DRIVEN DYNAMIC MODELS FOR SPATIAL INTERPOLATION OF WAVE ENERGY SOURCE CHARACTERISTICS: PRELIMINARY EXPERIMENTAL VALIDATION

World energy demand has increased significantly over recent decades, making clean and efficient energy production one of the most crucial challenges. In the context of renewable energy, wave energy coming from ocean and sea waves has emerged as one of the most promising sources, having a vast (yet untapped) potential (Astariz (2015)) of around 32 000 TWh/year (Mork (2010)). In contrast to other renewable energy technologies, such as wind or solar, technology convergence has been slow among wave energy converters (WECs). Among the challenges is the variability of the wave energy resource itself, with variations in amplitude and period, and directionality (Li (2021)). For this reason, accurate knowledge of metocean data is of paramount importance before installation for site selection, productivity assessment, and structural design; data are also crucial for installation, operation, maintenance and decommissioning. The metocean datasets needed to establish the correct deployment site must be comprehensive enough in terms of number, accuracy, time length, and breadth in space of the measurements. However, to obtain such amount and quality of data, extensive in-situ surveys (made through the installation of data buoys) are needed years before plant deployment and during operations, delaying the actual activities and demanding significant capital expenditure. In the presented work, a survey campaign is simulated in the wave basin facility at Aalborg University, Denmark. The obtained data are used to build dynamic models employed to estimate the wave source characteristics, assuming that, after an initial learning stage, most of the in-situ instrumentation is uninstalled and not available, and for this reason the remaining information must be obtained differently. Linear and nonlinear dynamic models are employed for this purpose, and compared on a validation set to assess their estimation capability.