Journal Article

Machine Learning-Based Forecasting of Metocean Data for Offshore Engineering Applications

Ghaderpour Taleghani, Shiva, Bahrami, Masoumeh, Velioglu Sogut, Deniz, Sedigh, Parviz, Barooni, Mohammad

2024

Overview

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.

• Publisher: MDPI AG
• Subject: Accuracy / Air pollution / Algorithms / Alternative energy sources / Atmospheric conditions / Climate change / deep learning / Deep water / Design / Efficiency / Electricity distribution / Energy management / Energy resources / Forecasting / Forecasting data / Forecasting techniques / Learning algorithms / Life span / Long short-term memory / Machine learning / metocean data forecast / Neural networks / Ocean models / Ocean wave heights / Offshore / Offshore energy sources / Offshore engineering / offshore wind turbine / Outdoor air quality / Renewable energy / Renewable energy sources / Renewable resources / Root-mean-square errors / Runoff / SARIMAX / Significant waves / Turbines / Water resources management / Wave height / Weather / Wind farms / Wind power / Wind speed / Wind speed forecasting / Wind turbines / Winds