A comprehensive study was conducted at a wave energy device test site located off the northeastern coast of Taiwan to assess the influence of oceanic currents on experimental equipment. A bottom-mounted 600 kHz acoustic Doppler current profiler, equipped with integrated temperature and pressure sensors, was deployed at a depth of approximately 31 m. This study, spanning from 6 June 2023 to 11 May 2024, recorded ocean current profiles by assembling data from 50 pings every 10 min, with a resolution of one meter per depth layer. The findings reveal that variations in water levels were predominantly influenced by the M2 tidal constituent, followed by the O1, K1, and S2 tides. Notably, seawater temperature fluctuations at the seabed were modulated by tides, especially the M2 tide. A significant drop in seawater temperature was also observed as the typhoon passed through the south of Taiwan. In terms of sea surface currents, the measured maximum current speed was 71.89 cm s⁻¹, but the average current speed was only 15.47 cm s⁻¹. Tidal currents indicated that the M4 and M2 tides were the most significant, with semimajor axes and inclination angles of 8.48 cm s⁻¹ and 102.60°, and 7.00 cm s⁻¹ and 97.76°, respectively. Seasonally, barotropic tidal currents were the strongest in winter. Additionally, internal tides were identified, with the first baroclinic mode being dominant. The zero-crossing depths varied between 14 and 18 m. During the summer, the M2 baroclinic tidal current displayed characteristics of the second baroclinic mode, with zero-crossing depths at approximately 7 m and 22 m. This node aligns with results from the empirical orthogonal function analysis and correlates with the depths’ significant shifts in seawater temperature as measured by a conductivity, temperature, and depth instrument. Despite the velocities of internal tides not being strong, the directional variance between surface and bottom flows presents critical considerations for the deployment and operation of moored wave energy devices.

The relationship between wind and waves has been extensively studied over time. However, understanding the local wind and wave relationship remains crucial for advancing renewable energy development and optimizing ocean management strategies. This study used wind and wave data collected by the ten weather buoys in the waters surrounding Taiwan to analyze regional sea states. The relationship between wind speed and significant wave height (SWH) was examined using regression analysis. Additionally, machine learning techniques were employed to assess the relative importance of features contributing to SWH growth. The regression analysis revealed that SWH in the waters surrounding Taiwan was not fully developed, with notable discrepancies observed between the waters east and west of Taiwan. According to the power law formula describing the relationship between wind speed and SWH, the eastern waters exhibited a larger prefactor coupled with a smaller scaling exponent, while the western waters manifested a converse parametric configuration. Through an evaluation of four machine learning algorithms, it was determined that wind speed is the most influential factor driving these regional differences, especially in the waters west of Taiwan. Beyond wind speed, air pressure or temperature emerged as the secondary feature factor governing wind–wave interactions in the waters east of Taiwan.