Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping
Source abstract
Abstract
As oil prices continue to rise internationally, shipping costs are also increasing rapidly. In order to reduce fuel costs, an economical shipping route must be determined by accurately predicting the estimated arrival time of ships. A common method in the evaluation of ship speed involves computing the total resistance of a ship using theoretical analysis; however, using theoretical equations cannot be applied for most ships under various operating conditions. In this study, a machine learning approach was proposed to predict ship speed over the ground using the automatic identification system (AIS) and noon-report maritime weather data. To train and validate the developed model, the AIS and marine weather data of the seventy-six vessels for a period one year were used. The model accuracy result shows that the proposed data-driven model has a satisfactory capability to predict the ship speed based on the chosen features.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Maritime Transport Emissions and Efficiency · Maritime Navigation and Safety · Ship Hydrodynamics and Maneuverability
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Research Institute of Mechanical Technology, Pusan Nat’l University, Busan 46241, Korea
School of Mechanical Engineering, Pusan Nat’l University, Busan 46241, Korea
Lab021, Busan 48508, Korea
Department of Naval Architecture & Ocean Engineering, Pusan Nat’l University, Busan 46241, Korea
Funding
National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) through GCRC-SOP · Korea government (MSIT) · Ministry of Trade, Industry & Energy (MOTIE) and Korea Institute for Advancement of Technology (KIAT)
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