The increasing interest in ocean-based renewable energy sources and the consequent need for advanced marine infrastructure have heightened the demand for accurate short-term forecasting of significant wave height (SWH). Given the impending investments in offshore infrastructure within the Baltic Sea, it becomes imperative to devise artificial intelligence strategies that can accurately predict wave parameters under the unique conditions of a shallow, tideless sea. This study presents an extensive evaluation of established machine learning techniques tailored to site specific wave spectra. We explore the effectiveness of artificial neural networks (ANNs) in the short-term prediction of SWH. Rigorous optimization of neural network hyperparameters is applied to improve forecast accuracy, with models validated on real-world datasets, proving their ability to predict SWH accurately across various forecast horizons. In addition, the performance of ANNs models is compared to optimized traditional forecasting methods, noting significant improvements in both accuracy and reliability. The results underscore the critical role of careful optimization in achieving precise short-term forecasts at targeted locations. They also suggest that even relatively simple models, when fine-tuned, can surpass the performance of more complex approaches commonly found in the literature. Building upon the foundational advancements in ANNs from the early 2000s, this research adopts a direct forecasting methodology utilizing data from marine observational platforms. Significantly, the networks are trained and validated with data directly obtained from a measurement buoy positioned in a deep water zone, devoid of seabed wave interactions. This aspect is particularly crucial for the strategic placement of future floating offshore farms, where these algorithms will be directly applied for short-term SWH predictions. Our findings underscore the importance of selecting optimal methods to ensure the utmost accuracy of short-term forecasts at targeted locations.
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