000010736 001__ 10736
000010736 005__ 20240627123825.0
000010736 0247_ $$2doi$$a10.24868/10736
000010736 245__ $$aArtificial Intelligence-based short-term forecasting of vessel performance parameters
000010736 269__ $$a2022-10-25
000010736 336__ $$aConference Proceedings
000010736 520__ $$aDeterministic 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, near-future performance prediction can substantially contribute to enhancing operational efficiency and enabling advanced system control.
Data from modern sensor technology, which has been becoming more readily available, combined with automatic control systems able to prescribe optimal control strategies can enhance 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.
In order 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 observed to achieve 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.
000010736 542__ $$fCC-BY-NC-ND
000010736 6531_ $$aNear-Term Forecasting
000010736 6531_ $$aMachine Learning
000010736 6531_ $$aElectric Power Generation
000010736 6531_ $$aHybrid Propulsion
000010736 6531_ $$aData-Driven Models
000010736 7001_ $$aValchev, I$$uUniversity of Strathclyde
000010736 7001_ $$aCoraddu, A$$uDelft University of Technology
000010736 7001_ $$aOneto, L$$uUniversita' Degli Studi di Genova	
000010736 7001_ $$aKalikatzarakis, K$$uDamen Naval
000010736 7001_ $$aTiddens, W$$uDirectorate of Materiel Sustainment, Royal Netherlands Navy
000010736 7001_ $$aGeertsma, R$$uFaculty of Military Sciences, Netherlands Defence Academy
000010736 773__ $$tConference Proceedings of iSCSS
000010736 773__ $$jiSCSS 2022
000010736 85641 $$uhttps://www.imarest.org/events/category/categories/imarest-event/international-ship-control-systems-symposium-2022$$yConference website
000010736 8564_ $$94452119f-37a9-4cff-bce2-31dda9b713ec$$s4546504$$uhttps://library.imarest.org/record/10736/files/10736.pdf
000010736 980__ $$aConference Proceedings