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A Data-Driven Approach for Generator Load Prediction in Shipboard Microgrid: The Chemical Tanker Case Study

Author

Listed:
  • Tayfun Uyanık

    (Maritime Faculty, Istanbul Technical University, 4469 Istanbul, Türkiye
    Center for Research on Microgrids, AAU Energy, 9220 Aalborg, Denmark)

  • Nur Najihah Abu Bakar

    (Center for Research on Microgrids, AAU Energy, 9220 Aalborg, Denmark)

  • Özcan Kalenderli

    (Faculty of Electrical and Electronics Engineering, Istanbul Technical University, 4469 Istanbul, Türkiye)

  • Yasin Arslanoğlu

    (Maritime Faculty, Istanbul Technical University, 4469 Istanbul, Türkiye)

  • Josep M. Guerrero

    (Center for Research on Microgrids, AAU Energy, 9220 Aalborg, Denmark)

  • Abderezak Lashab

    (Center for Research on Microgrids, AAU Energy, 9220 Aalborg, Denmark)

Abstract

Energy efficiency and operational safety practices on ships have gained more importance due to the rules set by the International Maritime Organization in recent years. While approximately 70% of the fuel consumed on a commercial ship is utilized for the propulsion load, a significant portion of the remaining fuel is consumed by the auxiliary generators responsible for the ship’s onboard load. It is crucial to comprehend the impact of the electrical load on the ship’s generators, as it significantly assists maritime operators in strategic energy planning to minimize the chance of unexpected electrical breakdowns during operation. However, an appropriate handling mechanism is required when there are massive datasets and varied input data involved. Thus, this study implements data-driven approaches to estimate the load of a chemical tanker ship’s generator using a 1000-day real dataset. Two case studies were performed, namely, single load prediction for each generator and total load prediction for all generators. The prediction results show that for the single generator load prediction of DG1, DG2, and DG3, the decision tree model encountered the least errors for MAE (0.2364, 0.1306, and 0.1532), RMSE (0.2455, 0.2069, and 0.2182), and MAPE (17.493, 5.1139, and 7.7481). In contrast, the deep neural network outperforms all other prediction models in the case of total generation prediction, with values of 1.0866, 2.6049, and 14.728 for MAE, RMSE, and MAPE, respectively.

Suggested Citation

  • Tayfun Uyanık & Nur Najihah Abu Bakar & Özcan Kalenderli & Yasin Arslanoğlu & Josep M. Guerrero & Abderezak Lashab, 2023. "A Data-Driven Approach for Generator Load Prediction in Shipboard Microgrid: The Chemical Tanker Case Study," Energies, MDPI, vol. 16(13), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:5092-:d:1184620
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    References listed on IDEAS

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    1. 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).
    2. McKenzie, Jordi, 2011. "Mean absolute percentage error and bias in economic forecasting," Economics Letters, Elsevier, vol. 113(3), pages 259-262.
    3. 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).
    4. Vahid Azizi & Guiping Hu, 2020. "Machine Learning Methods for Revenue Prediction in Google Merchandise Store," Springer Proceedings in Business and Economics, in: Hui Yang & Robin Qiu & Weiwei Chen (ed.), Smart Service Systems, Operations Management, and Analytics, pages 65-75, Springer.
    5. Hou, Jun & Sun, Jing & Hofmann, Heath, 2018. "Adaptive model predictive control with propulsion load estimation and prediction for all-electric ship energy management," Energy, Elsevier, vol. 150(C), pages 877-889.
    6. Choi, Sun & Kim, Young Jin, 2021. "Artificial neural network models for airport capacity prediction," Journal of Air Transport Management, Elsevier, vol. 97(C).
    7. Planakis, Nikolaos & Papalambrou, George & Kyrtatos, Nikolaos, 2022. "Ship energy management system development and experimental evaluation utilizing marine loading cycles based on machine learning techniques," Applied Energy, Elsevier, vol. 307(C).
    8. 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).
    9. Sidharth Sankhye & Guiping Hu, 2020. "Machine Learning Methods for Quality Prediction in Production," Logistics, MDPI, vol. 4(4), pages 1-19, December.
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