Vinnova - Shipping AI EEMs#

This project is funded by VINNOVA, jointed with Swedish Space Agency, FORMAS and Swedish Energy Agency!

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AI enhanced EEMs to reduce shipping emissions

Project background#

The International Maritime Organization (IMO) is targeting to reduce carbon intensity from shipping by at least 40% by 2030 compared to 2008. The American Bureau of Shipping energy outlook states that ship operation-related energy efficiency measures (EEMs) can help to reach the IMO 2030 emission goal. However, the benefits of those EEMs have not been fully realized in today’s market partly due to large uncertainties in a ship’s energy performance models, which are the key components for all the operation related EEMs. Driving by today’s digital transformation in shipping, large amounts of ship operation data are being collected. They can be exploited to significantly improve a ship’s performance models and the EEMs.

Project objective#

Concerning that a ship’s energy consumption and performance is strongly affected by MetOcean environments (espeically waves), this project will first construct wave correlation models by machine learning (ML) to improve the data quality of waves encountered by ships. Then, two hybrid quasi-static ship energy performance models will be developed to estimate a ship’s GHG emissions (energy cost) at stationary sea conditions, by combining physical models and data-based ML methods. Then, data-driven dynamic models will be developed to predict a ship’s dynamic energy performance (emissions) within stationary conditions. Those models built on AI-technologies will be implemented in an existing architecture onboard for EEMs, to assist optimal planning and decision-making during a ship’s life-cycle service. Finally, the AI-enhanced EEMs will be tested and commercialized to verify the climate objective of this project, i.e., reducing GHG emissions by about 20% from ships. Three shipping companies, as the project reference group, will give us the right to use their ship data to develop the AI-enhanced EEMs, and give feedback of testing the EEMs.

Our tasks at Chalmers#

  • Spatio-temporal model of metoceaen parameters for shipping

  • Develop AI based EEMs and digitalization