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Offshore wind energy is a sustainable renewable energy source that is acquired by harnessing the force of the wind offshore, where the absence of obstructions allows the wind to travel at higher and more steady speeds. Offshore wind has recently grown in popularity because wind energy is more powerful offshore than on land. Prior to the development of floating structures, wind turbines could not be deployed in particularly deep or complicated seabed locations since they were dependent on fixed structures. With the advent of floating structures, which are moored to the seabed using flexible anchors, chains, or steel cables, wind turbines can now be placed far offshore. The deployment of floating wind turbines in deep waters is encouraged by several benefits, including steadier winds, less visual impact, and flexible acoustic noise requirements. A thorough understanding of the physics underlying the dynamic response of the floating offshore wind turbines, as well as various design principles and analysis methods, is necessary to fully compete with traditional energy sources such as fossil fuels. The present work offers a comprehensive review of the most recent state-of-the-art developments in the offshore wind turbine technology, including aerodynamics, hydromechanics, mooring, ice, and inertial loads. The existing design concepts and numerical models used to simulate the complex wind turbine dynamics are also presented, and their capabilities and limitations are discussed in detail.

The advancement towards utilizing renewable energy sources is crucial for mitigating environmental issues such as air pollution and climate change. Offshore wind turbines, particularly floating offshore wind turbines (FOWTs), are developed to harness the stronger, steadier winds available over deep waters. Accurate metocean data forecasts, encompassing wind speed and wave height, are crucial for offshore wind farms’ optimal placement, operation, and maintenance and contribute significantly to FOWT’s efficiency, safety, and lifespan. This study examines the application of three machine learning (ML) models, including Facebook Prophet, Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX), and long short-term memory (LSTM), to forecast wind speeds and significant wave heights, using data from a buoy situated in the Pacific Ocean. The models are evaluated based on their ability to predict 1-, 3-, and 30-day future wind speed and wave height values, with performances assessed through Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) metrics. Among the models, LSTM displayed superior performance, effectively capturing the complex temporal dependencies in the data. Incorporating exogenous variables, such as atmospheric conditions and gust speed, further refined the predictions.The study’s findings highlight the potential of machine learning (ML) models to enhance the integration and reliability of renewable energy sources through accurate metocean forecasting.