Next Article in Journal
Spatial Differences and Influencing Factors of Carbon Emission Intensity in China’s Urban Agglomerations toward the Carbon Neutrality Target
Next Article in Special Issue
Data-Driven Weather Forecasting and Climate Modeling from the Perspective of Development
Previous Article in Journal
Exploring the Effects of Elevated Ozone Concentration on Physiological Processes in Summer Maize in North China Based on Exposure–Response Relationships
Previous Article in Special Issue
Tracking Carbon Dioxide with Lagrangian Transport Simulations: Case Study of Canadian Forest Fires in May 2021
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Mohammad Barooni
1,
Shiva Ghaderpour Taleghani
2,
Masoumeh Bahrami
3,
Parviz Sedigh
4 and
Deniz Velioglu Sogut
1,*
1
Ocean Engineering and Marine Sciences, Florida Institute of Technology, Melbourne, FL 32901, USA
2
School of Arts and Communication, Florida Institute of Technology, Melbourne, FL 32901, USA
3
Electrical and Computer Engineering, University of New Hampshire, Durham, NH 03824, USA
4
Mechanical Engineering, University of New Hampshire, Durham, NH 03824, USA
*
Author to whom correspondence should be addressed.
Atmosphere 2024, 15(6), 640; https://doi.org/10.3390/atmos15060640
Submission received: 25 April 2024 / Revised: 22 May 2024 / Accepted: 23 May 2024 / Published: 26 May 2024
(This article belongs to the Special Issue High-Performance Computing for Atmospheric Modeling)

Abstract

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.
Keywords: offshore wind turbine; deep learning; SARIMAX; metocean data forecast offshore wind turbine; deep learning; SARIMAX; metocean data forecast

Share and Cite

MDPI and ACS Style

Barooni, M.; Ghaderpour Taleghani, S.; Bahrami, M.; Sedigh, P.; Velioglu Sogut, D. Machine Learning-Based Forecasting of Metocean Data for Offshore Engineering Applications. Atmosphere 2024, 15, 640. https://doi.org/10.3390/atmos15060640

AMA Style

Barooni M, Ghaderpour Taleghani S, Bahrami M, Sedigh P, Velioglu Sogut D. Machine Learning-Based Forecasting of Metocean Data for Offshore Engineering Applications. Atmosphere. 2024; 15(6):640. https://doi.org/10.3390/atmos15060640

Chicago/Turabian Style

Barooni, Mohammad, Shiva Ghaderpour Taleghani, Masoumeh Bahrami, Parviz Sedigh, and Deniz Velioglu Sogut. 2024. "Machine Learning-Based Forecasting of Metocean Data for Offshore Engineering Applications" Atmosphere 15, no. 6: 640. https://doi.org/10.3390/atmos15060640

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop