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The increase in Greenhouse Gas (GHG) emissions due to shipping and its impact on
climate change is a concern for the maritime industry. Urgent steps are required to achieve
the International Maritime Organization (IMO) target of 50% GHG reduction by 2050.
The recent boom in digitalization of the shipping industry can be vital to improving
the energy efficiency of vessels. The high frequent operational data obtained from the
onboard sensors can be used to analyse the decrease in ship’s performance over time due
to aging and sufficient measures can be suggested timely to achieve the maximum possible
efficiency.
This master thesis investigates the performance of an intercontinental 14000 TEU
container vessel using high-frequency operational data. The operational data has been ob tained from University of Rostock server. Statistical outliers are removed using ISO19030
standards. Then this data is further filtered for different environmental and operational
conditions using simple filtering techniques and Machine Learning algorithms. Outputs
of both methods are compared with the design data to analyze the vessel’s performance
and the most suitable method is proposed. Moreover, the Energy Efficiency Operational
Indicator (EEOI) of the vessel is investigated over time. Results show that the perfor mance of the vessel is constant over the investigated time period. A good agreement has
also been found between the operational data with the design data.
Furthermore, this study also compares the performance of different machine learning
algorithms; Random Forrest, Decision Tree, Gradient Boosting, Multilayer Perceptron
and Least square methods over the filtered high-frequency data set. Blind testing results
show that the Random Forest algorithm has the best performance

Greenhouse Gas Emissions --- Vessel Performance Analysis --- Big Data Analytics --- Machine Learning --- Ingénierie, informatique & technologie > Ingénierie mécanique