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Svitzer Gaia Energy Efficiency
Photo: Johannes Hüffmeier

Data-driven Improved Energy Effciency of Ships

The aim of the study is to collect and analyze operational data from smaller vessels moving along the Swedish coast and identify energy savings potential of at least 10-35%. The result should be the basis for developing generic decision support tools at a later stage. Machine Learning, Maritime Gamification, Energy Efficiency, Shipping, MRV

Sweden has a target of becoming fossil free by 2045 and IMO aims to minimize emissions with 50% by 2050. While there is development ongoing when it comes to road transport, it goes slower in other parts of the transport sector. Increased energy efficiency has a huge potential, here the maritime sector lags many other industries. 90% of all data worldwide has been created in the last 2 years, ie a large part of new knowledge has been collected but is rarely analyzed.

The project aims to collect and analyse data from a range of smaller vessels to derive tools and services for achieving higher energy efficiency. At least five vessels will be analysed in detail to reduce their energy impact by 10-35%. Making use of smaller vessels with limited number of systems allows to identify drivers of high energy consumption, as the complexity of deriving useful correlation between outer and inner impacts and the energy efficiency is eased and spread to similar vessels is ensured,

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 there are no methods to analyze the complex systems. Developments in machine learning provide new opportunities to develop both technically and economically powerful tools for energy efficiency. Even today, to some extent, economic driving is applied, for example. eco-driving, however, the effect is limited in many cases as decision-making is more complex than the operator / navigator can overlook. In addition, there is not always incentive and motivation in individuals to reduce energy use. Data collection is increasing, but both quality review and analysis are not carried out to the same extent. Using the results of the project's data collection and analysis, recommendations can be given about which tools can be developed in the next step, e.g.

  • Nudging, decision support system or autopilot for ECO driving
  • Route optimization based on the ship's accelerations and movements
  • 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.)


Project name





Region Norrbotten, Region Skåne, Region Stockholm, Västra Götaland Region

RISE role in project

Research Lead, Participant

Project start


2 years

Total budget



Blueflow, Sjöfartsverket/ SMA, SSPA Sweden AB, Svitzer Sverige



Project website


Project members

Supports the UN sustainability goals

7. Affordable and clean energy
11. Sustainable cities and communities
13. Climate action