In this paper a new way to predict the propulsion power through the use of Big Data techniques is presented in order to improve the current way of evaluating the performance of a ship to lower emissions and for greener future operations. Big Data is a promising technological solution that can potentially improve the techniques used to evaluate ship performance by producing value from data. For Big Data techniques is intended the implementation of Machine Learning models for data analysis.
For the study, real data collected from a LCTC M/V is used and, in particular, the data concerning the performance of the hull. The features used to predict the propulsion are: speed over ground, speed through water, wind intensity and direction, course, heading, rudder angle, roll, pitch, forward and aft draft. This data works as the input for the Machine Learning for the prediction of the propulsive power. The Machine Learning models used are the XGBoost, short for eXtreme Gradient Boosting of the gradient boosted trees, and the Multi-layer perceptron, of the Neural Network. The models are taken from the Scikit-learn Python library (Pedregosa et al., 2011).
The data is divided in voyages, so that predictions of part of the voyages are made. The results are assessed with the R2 (coefficient of determination) and Mean Absolute Error, Machine Learning metrics, giving an accuracy around 10% depending on the voyage.
Ship performance in recent years has been given significant attention as a result of a series of measures ratified by the IMO. These measures aim at improving energy efficiency of ships and reducing green house gas (GHG) emissions.
In the light of these mandatory regulations the maritime community is seeking for solutions to increase the energy efficiency. One of the most promising measures to improve efficiency is to implement Big Data strategies to monitor a ship's performance. Big Data promises benefits based on insights from data for real-time monitoring, forecasting of events and improved performance management. One trend that can be seen across various industries, including the maritime industry, is the increased use of sensor data for performance monitoring, condition monitoring and optimization. This data, along with increased connection of ships and stakeholders in the maritime industry, due to more satellites and reduced prices that enable 24/7 ship to shore connection, is pushing for changes in the shipping industry.
