Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
143 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Time-Series Analysis Approach for Improving Energy Efficiency of a Fixed-Route Vessel in Short-Sea Shipping (2402.00698v1)

Published 1 Feb 2024 in cs.CE

Abstract: Several approaches have been developed for improving the ship energy efficiency, thereby reducing operating costs and ensuring compliance with climate change mitigation regulations. Many of these approaches will heavily depend on measured data from onboard IoT devices, including operational and environmental information, as well as external data sources for additional navigational data. In this paper, we develop a framework that implements time-series analysis techniques to optimize the vessel's speed profile for improving the vessel's energy efficiency. We present a case study involving a real-world data from a passenger vessel that was collected over a span of 15 months in the south of Sweden. The results indicate that the implemented models exhibit a range of outcomes and adaptability across different scenarios. The findings highlight the effectiveness of time-series analysis approach for optimizing vessel voyages within the context of constrained landscapes, as often seen in short-sea shipping.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (33)
  1. “Development of short sea shipping,” [Online]. Available: https://www7.transportation.gov/testimony/development-short-sea-shipping, Feb. 2007.
  2. P. Donner and T. Johansson, “Sulphur directive, short sea shipping and corporate social responsibility in a eu context,” corporate social responsibility in the maritime industry, pp. 149–166, 2018.
  3. J. D. Ampah, A. A. Yusuf, S. Afrane, C. Jin, and H. Liu, “Reviewing two decades of cleaner alternative marine fuels: Towards imo’s decarbonization of the maritime transport sector,” Journal of Cleaner Production, vol. 320, p. 128871, 2021.
  4. C. IMO, “Fourth imo ghg study 2020,” 2020.
  5. T. P. Zis, H. N. Psaraftis, and L. Ding, “Ship weather routing: A taxonomy and survey,” Ocean Engineering, vol. 213, p. 107697, 2020.
  6. H. Chen and P. Ballou, “Art and science of ship voyage optimization: A critical review,” 2021.
  7. L. Walther, A. Rizvanolli, M. Wendebourg, and C. Jahn, “Modeling and optimization algorithms in ship weather routing,” International Journal of e-Navigation and Maritime Economy, vol. 4, pp. 31–45, 2016.
  8. A. Fan, J. Yang, L. Yang, D. Wu, and N. Vladimir, “A review of ship fuel consumption models,” Ocean Engineering, vol. 264, p. 112405, 2022.
  9. M. H. Moradi, M. Brutsche, M. Wenig, U. Wagner, and T. Koch, “Marine route optimization using reinforcement learning approach to reduce fuel consumption and consequently minimize co2 emissions,” Ocean Engineering, vol. 259, p. 111882, 2022.
  10. A. Zakaria, A. Md Arof, and A. Khabir, “Instruments utilized in short sea shipping research: A review,” Design in Maritime Engineering, pp. 83–108, 2022.
  11. M. Grifoll, F. X. Martínez de Osés, and M. Castells, “Potential economic benefits of using a weather ship routing system at short sea shipping,” WMU Journal of Maritime Affairs, vol. 17, no. 2, pp. 195–211, 2018.
  12. T. W. Smith, J. Jalkanen, B. Anderson, J. Corbett, J. Faber, S. Hanayama, E. O’keeffe, S. Parker, L. Johansson, L. Aldous et al., “Third imo greenhouse gas study 2014,” 2015.
  13. P. R. Bellingmo, A. Pobitzer, U. Jørgensen, and S. P. Berge, “Energy efficient and safe ship routing using machine learning techniques on operational and weather data,” in 20th International Conference on Computer Applications and Information Technology in the Maritime Industries, 2021.
  14. U. Jørgensen, P. R. Belingmo, B. Murray, S. P. Berge, and A. Pobitzer, “Ship route optimization using hybrid physics-guided machine learning,” in Journal of Physics: Conference Series, vol. 2311, no. 1.   IOP Publishing, 2022, p. 012037.
  15. K. Sugimoto, “Digital twin for monitoring remaining fatigue life of critical hull structures.”
  16. M. Haranen, S. Myöhänen, and D. S. Cristea, “The role of accurate now-cast data in shi p efficiency analysis,” in 2nd Hull Performance & Insight Conference, 2017, pp. 25–38.
  17. M. Wingrove, “Owners cut fuel costs by 10 per cent,” vol. 10, no. 4, p. 42–43, 2016.
  18. J. Huotari, T. Manderbacka, A. Ritari, and K. Tammi, “Convex optimisation model for ship speed profile: Optimisation under fixed schedule,” Journal of Marine Science and Engineering, vol. 9, no. 7, p. 730, 2021.
  19. K. Wang, J. Wang, L. Huang, Y. Yuan, G. Wu, H. Xing, Z. Wang, Z. Wang, and X. Jiang, “A comprehensive review on the prediction of ship energy consumption and pollution gas emissions,” Ocean Engineering, vol. 266, p. 112826, 2022.
  20. M. Abuella, M. A. Atoui, S. Nowaczyk, S. Johansson, and E. Faghani, “Data-driven explainable artificial intelligence for energy efficiency in short-sea shipping,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases.   Springer, 2023, pp. 226–241.
  21. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997.
  22. R. C. Staudemeyer and E. R. Morris, “Understanding lstm–a tutorial into long short-term memory recurrent neural networks,” arXiv preprint arXiv:1909.09586, 2019.
  23. T. Cover and P. Hart, “Nearest neighbor pattern classification,” IEEE transactions on information theory, vol. 13, no. 1, pp. 21–27, 1967.
  24. D. J. Berndt and J. Clifford, “Using dynamic time warping to find patterns in time series,” in Proceedings of the 3rd international conference on knowledge discovery and data mining, 1994, pp. 359–370.
  25. E. Keogh and C. A. Ratanamahatana, “Exact indexing of dynamic time warping,” Knowledge and information systems, vol. 7, pp. 358–386, 2005.
  26. H. Ding, G. Trajcevski, P. Scheuermann, X. Wang, and E. Keogh, “Querying and mining of time series data: experimental comparison of representations and distance measures,” Proceedings of the VLDB Endowment, vol. 1, no. 2, pp. 1542–1552, 2008.
  27. T. Rakthanmanon, B. Campana, A. Mueen, G. Batista, B. Westover, Q. Zhu, J. Zakaria, and E. Keogh, “Searching and mining trillions of time series subsequences under dynamic time warping,” in Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 2012, pp. 262–270.
  28. A. Bagnall, J. Lines, A. Bostrom, J. Large, and E. Keogh, “The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances,” Data mining and knowledge discovery, vol. 31, pp. 606–660, 2017.
  29. L. R. Rabiner, “A tutorial on hidden markov models and selected applications in speech recognition,” Proceedings of the IEEE, vol. 77, no. 2, pp. 257–286, 1989.
  30. “CetaSol AB ,” [Online]. Available: https://cetasol.com.
  31. “Marine Traffic,” [Online]. Available: https://www.marinetraffic.com/en/ais/details/ships/shipid:1088282/mmsi:265513810/imo:8602713/vessel:BURO, Jul. 2022.
  32. “Copernicus Marine Service ,” [Online]. Available: https://marine.copernicus.eu.
  33. “StormGlass API ,” [Online]. Available: https://stormglass.io.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now