The number of installed floating offshore wind turbines (FOWTs) has doubled since 2017, quadrupling the total installed capacity, and is expected to increase significantly over the next decade. Consequently, there is a growing consideration towards the main challenges for FOWT projects: monitoring the system’s integrity, extending the lifespan of the components, and maintaining FOWTs safely at scale. Effectively and efficiently addressing these challenges would unlock the wide-scale deployment of FOWTs. In this work, we focus on one of the most critical components of the FOWTs, the Mooring Lines (MoLs), which are responsible for fixing the structure to the seabed. The primary mechanical failure mechanisms in MoLs are extreme load and fatigue, both of which are functions of the axial tension. An effective solution to detect long-term drifts in the mechanical response of the MoLs is to develop a Digital Twin (DT) able to accurately predict the behaviour of the healthy system to compare with the actual one. Moreover, we will develop another DT able to accurately predict the near future axial tension as an effective tool to improve the lifespan of the MoLs and the safety of FOWT maintenance operations. In fact, by changing the FOWT operational settings, according to the DT prediction, operators can increase the lifespan of the MoLs by reducing the stress and, additionally, in the case where FOWT operational maintenance is in progress, the prediction from the DT can serve as early safety warning to operators. Authors will leverage operational data collected from the world’s first commercial floating-wind farm [the Hywind Pilot Park in 2018, to investigate the effectiveness of DTs for the prediction of the MoL axial tension for the two scenarios depicted above. The DTs will be developed using state-of-the-art data-driven methods, and results based on real operational data will support our proposal.

The purpose of this chapter is to provide an overview of the state-of-the-art and future perspectives of Data Science and Advanced Analytics for Shipping Energy Systems. Specifically, we will start by listing the different static and dynamic data sources and knowledge base available in this particular context. Then we will review the Data Science and Advanced Analytics technologies that can leverage these data to extract and synthesize new additional actionable information, suggestions, and actions. We will then review the current exploitation strategies of these technologies aiming at improving the current Shipping Energy Systems. In conclusion, we will depict our vision on the future perspectives of the application and adoption of Data Science and Advanced Analytics for shaping the next generations of Shipping Energy Systems.

The number of installed Floating Offshore Wind Turbines (FOWTs) has doubled since 2017, quadrupling the total installed capacity, and is expected to increase significantly over the next decade. Consequently, there is a growing consideration towards the main challenges for FOWT projects: monitoring the system’s integrity, extending the lifespan of the components, and maintaining FOWTs safely at scale. Effectively and efficiently addressing these challenges would unlock the wide-scale deployment of FOWTs. In this work, we focus on one of the most critical components of the FOWTs, the Mooring Lines (MoLs), which are responsible for fixing the structure to the seabed. The primary mechanical failure mechanisms in MoLs are extreme load and fatigue, both of which are functions of the axial tension. An effective solution to detect long term drifts in the mechanical response of the MoLs is to develop a Digital Twin (DT) able to accurately predict the behaviour of the healthy system to compare with the actual one. Authors will leverage operational data collected from the world’s first commercial floating wind farm (Hywind Pilot Park) in 2018, to investigate the effectiveness of the DT for the prediction of the MoL axial tension. The DT will be developed using state-of-the-art data-driven methods, and results based on real operational data will support our proposal.