Artificial Intelligence-based short-term forecasting of vessel performance parameters

I. Valchev, A. Coraddu*, L. Oneto, M. Kalikatzarakis, W. Tiddens, R.D. Geertsma

*Corresponding author for this work

Research output: Contribution to journalConference articleScientificpeer-review

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Deterministic models based on the laws of physics, as well as data-driven models, are often used to assess the current state of vessels and their systems, as well as predict their future behaviour. Predictive maintenance methodologies (i.e., Condition Based Maintenance) and advanced control strategies (i.e., Model Predictive Control) are built upon the use of such numerical tools to identify ensuing performance shifts. In fact, forecasting near-future performance can substantially contribute to enhancing operational efficiency and enabling advanced system control. Data from modern sensor technology, which has become more readily available, combined with automatic control systems capable of prescribing optimal control strategies, can improve vessel operation and reduce energy consumption. A data-driven model that relies on recent advances in Artificial Intelligence, Machine Learning, and Data Mining, leveraging historical observations is employed to forecast a vessel’s onboard power generation trends as a function of the past, present, and future behaviour of a ship and its systems. To prove the framework, the proposed methodology is tested on real data collected from the Integrated Platform Management System of an Oceangoing Patrol Vessel of the Royal Netherlands Navy. The developed data-driven model is achieves high forecasting accuracy in the near-term. The authors foresee that the proposed methodology could be used as part of an electric energy control strategy, within a more integrated and intelligent mission planning framework.

Original languageEnglish
Article number31
Number of pages13
JournalProceedings of the International Ship Control Systems Symposium
Publication statusPublished - 2022
Event16th International Naval Engineering Conference and Exhibition incorporating the International Ship Control Systems Symposium, INEC/iSCSS 2022 - Aula Congress Centre, Delft University of Technology, The Netherlands, Delft, Netherlands
Duration: 8 Nov 202210 Nov 2022
Conference number: 16



  • Near-Term Forecasting
  • Machine Learning
  • Electric Power Generation
  • Hybrid Propulsion
  • Data-Driven Models


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