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

Author

Listed:
  • Tayfun Uyanık

    (Maritime Faculty, Istanbul Technical University, Tuzla, İstanbul 34940, Turkey
    Center for Research on Microgrids, AAU Energy, Aalborg University, 9220 Aalborg, Denmark)

  • Yunus Yalman

    (Center for Research on Microgrids, AAU Energy, Aalborg University, 9220 Aalborg, Denmark
    Faculty of Engineering and Natural Sciences, Ankara Yıldırım Beyazıt University, Çubuk, Ankara 06760, Turkey)

  • 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)

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.

Suggested Citation

  • Tayfun Uyanık & Yunus Yalman & Özcan Kalenderli & Yasin Arslanoğlu & Yacine Terriche & Chun-Lien Su & Josep M. Guerrero, 2022. "Data-Driven Approach for Estimating Power and Fuel Consumption of Ship: A Case of Container Vessel," Mathematics, MDPI, vol. 10(22), pages 1-21, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4167-:d:966053
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    References listed on IDEAS

    as
    1. Ueki, Masao, 2021. "Testing conditional mean through regression model sequence using Yanai’s generalized coefficient of determination," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    2. ArunKumar, K.E. & Kalaga, Dinesh V. & Kumar, Ch. Mohan Sai & Kawaji, Masahiro & Brenza, Timothy M, 2021. "Forecasting of COVID-19 using deep layer Recurrent Neural Networks (RNNs) with Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) cells," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    3. Harilaos N. Psaraftis, 2019. "Speed Optimization vs Speed Reduction: the Choice between Speed Limits and a Bunker Levy," Sustainability, MDPI, vol. 11(8), pages 1-18, April.
    4. Thomson, Heather & Corbett, James J. & Winebrake, James J., 2015. "Natural gas as a marine fuel," Energy Policy, Elsevier, vol. 87(C), pages 153-167.
    5. Bakar, Nur Najihah Abu & Bazmohammadi, Najmeh & Çimen, Halil & Uyanik, Tayfun & Vasquez, Juan C. & Guerrero, Josep M., 2022. "Data-driven ship berthing forecasting for cold ironing in maritime transportation," Applied Energy, Elsevier, vol. 326(C).
    6. Wu, Yuankai & Tan, Huachun & Peng, Jiankun & Zhang, Hailong & He, Hongwen, 2019. "Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 247(C), pages 454-466.
    7. Choi, Sun & Kim, Young Jin, 2021. "Artificial neural network models for airport capacity prediction," Journal of Air Transport Management, Elsevier, vol. 97(C).
    8. Olson, Luke M. & Qi, Min & Zhang, Xiaofei & Zhao, Xinlei, 2021. "Machine learning loss given default for corporate debt," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 144-159.
    9. Xiaohong, Dai & Huajiang, Chen & Bagherzadeh, Seyed Amin & Shayan, Masoud & Akbari, Mohammad, 2020. "Statistical estimation the thermal conductivity of MWCNTs-SiO2/Water-EG nanofluid using the ridge regression method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    10. Yan, Ran & Wang, Shuaian & Du, Yuquan, 2020. "Development of a two-stage ship fuel consumption prediction and reduction model for a dry bulk ship," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    11. Yunus Yalman & Tayfun Uyanık & İbrahim Atlı & Adnan Tan & Kamil Çağatay Bayındır & Ömer Karal & Saeed Golestan & Josep M. Guerrero, 2022. "Prediction of Voltage Sag Relative Location with Data-Driven Algorithms in Distribution Grid," Energies, MDPI, vol. 15(18), pages 1-16, September.
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