About the Project

The data analysis of energy consumption is often complex and there are different driving forces for decisions. However, increased data collection can be unprofitable if you do not have methods to analyze the complex systems. Developments within machine learning provides new opportunities to develop both technically and economically powerful tools energy efficiency.
Even today, to some extent, economic driving is applied, for example. eco-driving, however, the effect is in many cases limited as decision-making is more complex than the operator / navigator can see. Also, not always available incentives and motivation of individuals to reduce energy use. However, data collection is increasing
both quality review and analysis are not performed to the same extent.
o Using the results of the project’s data collection and analysis, recommendations can be given about which tools which can be developed in a next step, e.g.
o Nudging, decision support system or autopilot for ECO driving
o Route optimization based on the ship’s accelerations and motions
o Decision support based on statistics or. real-time analysis of data to identify optimal operation (parameters such as sea state, current, speed, load condition, etc.)

The objectives of the project are to:

– Achieve reduced energy use on the project’s vessels by 10–35% both at quay and in sea operations.
– Demonstrate potential with machine learning of operational data.
– Demonstrate the possibility that better operational data may form the basis for the development of generic energy efficiency tools for smaller vessels in commercial traffic.
– Create an open AI database with operational data from a number of different types of smaller craft.
– Investigate the possibilities for reduced energy use through “gamification” of ship operations

The project consortium and its partners consist of the entire value chain from end-users to technology providers. The project is lead by one of the SME:s in the project. The project is co-financed by the Swedish Energy Agency/ Energimyndigheten.


Co-financed by

Swedish Energy Agency


Mathias Johanson, Alkit Communications

Johannes Hüffmeier, RISE Research Institutes of Sweden