Data-Driven Approach for Estimating Power and Fuel Consumption of Ship: A Case of Container Vessel

Tayfun Uyanık (), Yunus Yalman, Özcan Kalenderli, Yasin Arslanoğlu, Yacine Terriche, Chun-Lien Su () and Josep M. Guerrero
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Tayfun Uyanık: Maritime Faculty, Istanbul Technical University, Tuzla, İstanbul 34940, Turkey
Yunus Yalman: Center for Research on Microgrids, AAU Energy, Aalborg University, 9220 Aalborg, Denmark
Özcan Kalenderli: Faculty of Electrical and Electronics Engineering, Istanbul Technical University, Maslak, İstanbul 34469, Turkey
Yasin Arslanoğlu: Maritime Faculty, Istanbul Technical University, Tuzla, İstanbul 34940, Turkey
Yacine Terriche: Center for Research on Microgrids, AAU Energy, Aalborg University, 9220 Aalborg, Denmark
Chun-Lien Su: Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 807618, Taiwan
Josep M. Guerrero: Center for Research on Microgrids, AAU Energy, Aalborg University, 9220 Aalborg, Denmark

Mathematics, 2022, vol. 10, issue 22, 1-21

Abstract: In recent years, shipborne emissions have become a growing environmental threat. The International Maritime Organization has implemented various rules and regulations to resolve this concern. The Ship Energy Efficiency Management Plan, Energy Efficiency Design Index, and Energy Efficiency Operational Indicator are examples of guidelines that increase energy efficiency and reduce shipborne emissions. The main engine shaft power (MESP) and fuel consumption (FC) are the critical components used in ship energy efficiency calculations. Errors in ship energy efficiency calculation methodologies are also caused by misinterpretation of these values. This study aims to predict the MESP and FC of a container ship with the help of data-driven methodologies utilizing actual voyage data to assist in the calculation process of the ship’s energy efficiency indexes appropriately. The algorithms’ prediction success was measured using the RMSE, MAE, and R 2 error metrics. When the simulation results were analyzed, the Deep Neural Network and Bayes algorithms predicted MESP best with 0.000001 and 0.000002 RMSE, 0.000987 and 0.000991 MAE, and 0.999999 R 2 , respectively, while the Multiple-Linear Regression and Kernel Ridge algorithms estimated FC best with 0.000208 and 0.000216 RMSE, 0.001375 and 0.001471 MAE, and 0.999999 R 2 , respectively.

Keywords: fuel consumption; energy efficiency; machine learning; deep neural network; power prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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