CPL - Chalmers Publication Library
| Utbildning | Forskning | Styrkeområden | Om Chalmers | In English In English Ej inloggad.

Statistical models for the speed prediction of a container ship

Wengang Mao (Institutionen för sjöfart och marin teknik, Marin teknik) ; Igor Rychlik (Institutionen för matematiska vetenskaper, matematisk statistik) ; Jonas Wallin (Institutionen för matematiska vetenskaper, matematisk statistik) ; Gaute Storhaug
Ocean Engineering (0029-8018). Vol. 126 (2016), p. 152-162.
[Artikel, refereegranskad vetenskaplig]

Accurate prediction of ship speed for given engine power and encountering sea environments is one of the key factors for ship route planning to ensure expected time of arrivals (ETA). Traditional methods need first to compute a ship's total resistance based on theoretical calculations, which are often associated with large uncertainties. In this paper, two statistical approaches are investigated to establish models for a ship's speed prediction. The measurement data of a containership during one year's sailing are used for the demonstration and validation of the presented statistical methods. The pros and cons of the methods are compared in terms of capability, robustness, and accuracy of the prediction. By means of the measured engine Revolutions Per Minute (RPM) and extracted sea environments along the ship's sailing routes, the statistical methods are shown to be able to give reliable speed predictions. Further investigation is needed to test the capability of the statistical methods for the speed prediction using engine power instead of RPM.

Nyckelord: Performance measurement; Ship speed prediction; Engine RPM; Regression; Autoregressive model; Mixed effects model

Den här publikationen ingår i följande styrkeområden:

Läs mer om Chalmers styrkeområden  

Denna post skapades 2016-09-21. Senast ändrad 2017-07-03.
CPL Pubid: 242091


Läs direkt!

Länk till annan sajt (kan kräva inloggning)

Institutioner (Chalmers)

Institutionen för sjöfart och marin teknik, Marin teknik (2015-2017)
Institutionen för matematiska vetenskaper, matematisk statistik (2005-2016)



Chalmers infrastruktur