Accurate prediction of extreme waves, particularly the maximum wave height and the ratio between the maximum and significant wave heights of individual waves, is crucial for maritime safety and the resilience of offshore infrastructure. This study employs machine learning (ML) techniques such as linear regression modeling (LM), support vector regression (SVR), long short-term memory (LSTM), and gated recurrent units (GRU) to develop predictive models based on historical data (1990–2024) obtained from a buoy at a specific oceanic location. The results show that the SVR model provides the highest accuracy in predicting the maximum wave height ($H_{max}$), achieving a coefficient of determination ($R^2$) of 0.9006 and mean squared error (MSE) of 0.0185. For estimation of the ratio between maximum and significant wave heights ($H_{max}/H_s$), the SVR and LM models exhibit comparable performance, with MSE values of 0.0229. These findings have significant implications for improving early warning systems, optimizing the structural design of offshore infrastructure, and enhancing the efficiency of energy extraction under changing climate conditions.

In this study, we explore long-term trends in wave loads on cylindrical monopile structures in the North Atlantic Ocean off western Ireland, utilizing significant wave height and peak wave period values from ERA20 reanalysis data, calibrated against ERA5 from the Copernicus website. Wave excitation on fixed monopile structures is evaluated, with monopiles being an essential component for wave energy converters and offshore wind turbines. The benchmark 5MW National Renewable Energy Laboratory (NREL) fixed monopile turbine (OC3) is used to analyze drag forces, inertial forces, and fore-aft moments, revealing significant increases: Drag force up to 77%, inertial force up to 17%, and bending moment up to 16% over 110 years. The wave loads exerted by waves on the monopile are compared to the dynamic response of the monopile, via OpenFAST. An analysis of extreme waves indicates scalable long-term trends across different sea states.

Ireland is the first landmass between the northeast Atlantic Ocean and Europe; its geographical location endows it with high energy potential in the ocean. At present, around 40% of the energy in Ireland is generated by renewable technologies, the majority of which is produced by onshore wind turbines. However, given the marine energy potential, the integration of wave energy converters into the future energy mix is a plausible proposition. This integration would result in a more stable energy mix, which is not reliant upon a single renewable resource. This study aims to analyse the wave power potential in Ireland by employing long-term satellite altimeter and weather buoy observations. Satellite altimeters offer long-term measurements, and cover a broader area compared to weather buoys. Data spanning from 1995 until 2024 is employed, encompassing a total of 30-years. This timeframe is particularly pertinent in the context of the analysis, as it encompasses the projected lifespan of the wave energy converters to be potentially installed. The goal is to assess whether decadal variations on the available wave power would affect the devices’ performance. To this end, wave power variation maps are generated. As satellite altimeters do not measure wave period, a regression following the method proposed by Gommenginger et. al. (2003) is employed to estimate the zero-crossing wave period by relating the significant wave height and the back-scatter coefficient measured by the altimeters, with Irish moored buoy observations. The wave energy period is obtained from wave period ratio maps created from results by Haoyu Jiang et. al. (2022); ultimately wave power is calculated assuming deep water and panchromatic seas. Decadal variations are calculated with via Theil-Sen approach, after removing the lag-1 autocorrelation, and the statistical significance its evaluated by means of the Mann Kendall test.