TY  - GEN
N2  - 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, 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.
DO  - 10.24868/10736
DO  - doi
AB  - 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, 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.
AD  - University of Strathclyde
AD  - Delft University of Technology
AD  - Universita' Degli Studi di Genova	
AD  - Damen Naval
AD  - Directorate of Materiel Sustainment, Royal Netherlands Navy
AD  - Faculty of Military Sciences, Netherlands Defence Academy
T1  - Artificial Intelligence-based short-term forecasting of vessel performance parameters
DA  - 2022-10-25
AU  - Valchev, I
AU  - Coraddu, A
AU  - Oneto, L
AU  - Kalikatzarakis, K
AU  - Tiddens, W
AU  - Geertsma, R
L1  - https://library.imarest.org/record/10736/files/10736.pdf
JF  - Conference Proceedings of iSCSS
VL  - iSCSS 2022
PY  - 2022-10-25
ID  - 10736
L4  - https://library.imarest.org/record/10736/files/10736.pdf
KW  - Near-Term Forecasting
KW  - Machine Learning
KW  - Electric Power Generation
KW  - Hybrid Propulsion
KW  - Data-Driven Models
TI  - Artificial Intelligence-based short-term forecasting of vessel performance parameters
Y1  - 2022-10-25
L2  - https://library.imarest.org/record/10736/files/10736.pdf
LK  - https://www.imarest.org/events/category/categories/imarest-event/international-ship-control-systems-symposium-2022
LK  - https://library.imarest.org/record/10736/files/10736.pdf
UR  - https://www.imarest.org/events/category/categories/imarest-event/international-ship-control-systems-symposium-2022
UR  - https://library.imarest.org/record/10736/files/10736.pdf
ER  -