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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 R2 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 R2, 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 R2, respectively.
|Publication status||Published - Nov 2022|
Bibliographical noteFunding Information:
This study was supported by The Scientific and Technological Research Council of Turkey BIDEB 2214-A International Doctoral Research Fellowship Programme. The research completed by Chun-Lien Su was funded by the Ministry of Science and Technology of Taiwan under Grant MOST 107-2221-E-992-073-MY3.
© 2022 by the authors.
- deep neural network
- energy efficiency
- fuel consumption
- machine learning
- power prediction
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CROM: Center for Research on Microgrids
Guerrero, J. M., Vasquez, J. C., Tinajero, G. D. A., Akhavan, A. & Guldbæk, B. K.
01/08/2019 → 31/07/2025