TY - GEN
T1 - Prediction of Ship Fuel Consumption Due to the Effect of Weather Conditions
AU - Megawati, Sintia
AU - Aisjah, Aulia Siti
AU - Widjaja, Sjarief
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The route planning for ship navigation, carried out by navigators and captains, considers various factors, including the weather conditions in the navigated waters. This is because weather conditions are one of the factors that can affect the resistance experienced by the ship. Poor weather conditions can increase the ship's resistance, resulting in increased fuel consumption and posing a threat to the safety of the ship. Predicting fuel consumption while considering weather conditions is a research topic that has been the focus of many researchers. Various methods, especially artificial intelligence methods, are used for prediction, but the artificial neural network (ANN) method provides the best performance among the methods used. ANN could provide the best performance because it can model the relationships of a complex and nonlinear system, which cannot be converted into mathematical equations. Weather factors have been used as one of the input variables in previous studies, so this paper proposes considering weather factors as disturbance factors. This paper can serve as a guide for researchers to further develop the ANN method as one of the methods for predicting ship fuel consumption while considering weather conditions. The paper presents a comparison of the architectural models of ANN that have been used and how each architecture performs.
AB - The route planning for ship navigation, carried out by navigators and captains, considers various factors, including the weather conditions in the navigated waters. This is because weather conditions are one of the factors that can affect the resistance experienced by the ship. Poor weather conditions can increase the ship's resistance, resulting in increased fuel consumption and posing a threat to the safety of the ship. Predicting fuel consumption while considering weather conditions is a research topic that has been the focus of many researchers. Various methods, especially artificial intelligence methods, are used for prediction, but the artificial neural network (ANN) method provides the best performance among the methods used. ANN could provide the best performance because it can model the relationships of a complex and nonlinear system, which cannot be converted into mathematical equations. Weather factors have been used as one of the input variables in previous studies, so this paper proposes considering weather factors as disturbance factors. This paper can serve as a guide for researchers to further develop the ANN method as one of the methods for predicting ship fuel consumption while considering weather conditions. The paper presents a comparison of the architectural models of ANN that have been used and how each architecture performs.
KW - ANN
KW - Fuel Oil Consumption
KW - Prediction
KW - Ship
KW - Weather
UR - http://www.scopus.com/inward/record.url?scp=85171168311&partnerID=8YFLogxK
U2 - 10.1109/ISITIA59021.2023.10221158
DO - 10.1109/ISITIA59021.2023.10221158
M3 - Conference contribution
AN - SCOPUS:85171168311
T3 - 2023 International Seminar on Intelligent Technology and Its Applications: Leveraging Intelligent Systems to Achieve Sustainable Development Goals, ISITIA 2023 - Proceeding
SP - 786
EP - 791
BT - 2023 International Seminar on Intelligent Technology and Its Applications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023
Y2 - 26 July 2023 through 27 July 2023
ER -