ISEA -- Increase shipping efficiency using ship data analytics and AI to assist ship operations
Various energy efficiency measures (EEMs) have been used in the shipping market, but their potentials to reduce fuel consumption and air emissions are not fully recognized partly due to uncertain ship performance models used in those EEMs. The project aims at identifying shipping EEMs that can be significantly improved by implementing data analytics and AI in different components of those EEMs through the demonstration of their integration into the
IMO Just-In-Time (JIT) arrival guidance. By analyzing actual benefits of using big data analytics and AI in a ship’s EEMs, this project is expected to help further reduce fuel consumption/ emissions by promoting the upgrading and utilization of AI integrated shipping EEMs.
From social perspectives, by studying the capability, willingness and barriers to use AI assisting ship operations, this project will look for AI integrated solutions to help smoothen implementation and utilization of the EEMs without introducing extra workload/burdens to seafarers, and assist decision making processes to reduce pressure for ship masters onboard.
From economic perspectives, this project will not just help to increase the capability of fuel savings by integrated AI in those EEMs, but also promote utilization of digitalization and big data analytics in shipping contributing to active research, innovation and development activities for sustainable shipping. Furthermore, the AI integrated solutions can contribute to more accurate ship operational management and better utilization of ships leading to reduced costs for staff, fuel and depreciation.
- University of Gothenburg (Academic, Sweden)
- University of Gothenburg (Publisher, Sweden)
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- Swedish Transport Administration (Public, Sweden)
- Lighthouse (Centre, Sweden)