Sequential Data-Driven Long-Term Weather Forecasting Models’ Performance Comparison for Improving Offshore Operation and Maintenance Operations

Ravi Pandit (), Davide Astolfi, Anh Minh Tang and David Infield
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Ravi Pandit: Centre for Life-Cycle Engineering and Management, Cranfield University, Bedford MK43 0AL, UK
Davide Astolfi: Department of Engineering, University of Perugia, Via G. Duranti, 06125 Perugia, Italy
Anh Minh Tang: École des Ponts ParisTech (ENPC), Ministry for the Ecological Transition, 77420 Paris, France
David Infield: Electronics and Electrical Engineering Department, University of Strathclyde, Glasgow MK43 0AL, UK

Energies, 2022, vol. 15, issue 19, 1-20

Abstract: Offshore wind turbines (OWTs), in comparison to onshore wind turbines, are gaining popularity worldwide since they create a large amount of electrical power and have thus become more financially viable in recent years. However, OWTs are costly as they are vulnerable to damage from extremely high-speed winds and thereby affect operation and maintenance (O&M) operations (e.g., vessel access, repair, and downtime). Therefore, accurate weather forecasting helps to optimise wind farm O&M operations, improve safety, and reduce the risk for wind farm operators. Sequential data-driven models recently found application in solving the wind turbines problem; however, their application to improve offshore operation and maintenance through weather forecasting is still limited and needs further investigation. This paper fills this gap by proposing three sequential data-driven techniques, namely, long short-term memory (LSTM), bidirectional LSTM (BiLSTM) and gated recurrent units (GRU) for long-term weather forecasting. The proposed techniques are then compared to summarise the strength and weaknesses of these models concerning long-term weather forecasting. Weather datasets (wind speed and wave height) are intermittent over different time scales and reflect offshore weather conditions. These datasets (obtained from the FINO3 database) will be used in this study for training and validation purposes. The study results suggest that the proposed technique can generate more realistic and reliable weather forecasts in the long term. It can also be stated that it responds better to seasonality and forecasted expected results. This is further validated by the calculated values of statistical performance metrics and uncertainty quantification.

Keywords: wind turbine; offshore wind; weather forecasting; deep learning; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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