Title
Artificial Intelligence-based short-term forecasting of vessel performance parameters
Author
Valchev, I. (University of Strathclyde)
Coraddu, A. (TU Delft Ship Design, Production and Operations) 
Oneto, L. (University of Genova)
Kalikatzarakis, M. (Damen Naval)
Tiddens, W. (Royal Netherlands Navy)
Geertsma, R.D. (Netherlands Defence Academy)
Date
2022
Abstract
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.
Subject
Near-Term Forecasting
Machine Learning
Electric Power Generation
Hybrid Propulsion
Data-Driven Models
To reference this document use:
http://resolver.tudelft.nl/uuid:a582ed5e-6847-435d-a279-811a306a0043
DOI
https://doi.org/10.24868/10736
ISSN
2631-8741
Source
Proceedings of the International Ship Control Systems Symposium, 16
Event
16th International Naval Engineering Conference and Exhibition incorporating the International Ship Control Systems Symposium, INEC/iSCSS 2022, 2022-11-08 → 2022-11-10, Aula Congress Centre, Delft University of Technology, The Netherlands, Delft, Netherlands
Part of collection
Institutional Repository
Document type
journal article
Rights
© 2022 I. Valchev, A. Coraddu, L. Oneto, M. Kalikatzarakis, W. Tiddens, R.D. Geertsma