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Machine Learning-Based Extraction Method for Marine Load Cycles with Environmentally Sustainable Applications

Author

Listed:
  • Xiaojun Sun

    (School of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121001, China)

  • Yingbo Gao

    (School of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121001, China)

  • Qiao Zhang

    (School of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121001, China)

  • Shunliang Ding

    (School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China)

Abstract

The current lack of harmonized standard test conditions for marine shipping hinders the comparison of performance and compliance assessments for different types of ships. This article puts forward a method for extracting ship loading cycles using machine learning algorithms. Time-series data are extracted from real ships in operation, and a segmented linear approximation method and a data normalization technique are adopted. A hierarchical-clustering type of soft dynamic time-warping similarity analysis method is presented to efficiently analyze the similarity of different time-series data, using soft dynamic time warping (Soft-DTW) combined with hierarchical clustering algorithms from the field of machine learning. The problem of data bias caused by spatial and temporal offset characteristics is effectively solved in marine test condition data. The validity and reliability of the proposed method are validated through the analysis of case data. The results demonstrate that the hierarchically clustered soft dynamic time-warping similarity analysis method can be considered reliable for obtaining test cases with different characteristics. Furthermore, it provides input conditions for effectively identifying the operating conditions of different types of ships with high levels of energy consumption and high emissions, thus allowing for the establishment of energy-saving and emissions-reducing sailing strategies.

Suggested Citation

  • Xiaojun Sun & Yingbo Gao & Qiao Zhang & Shunliang Ding, 2024. "Machine Learning-Based Extraction Method for Marine Load Cycles with Environmentally Sustainable Applications," Sustainability, MDPI, vol. 16(11), pages 1-21, June.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:11:p:4840-:d:1409661
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    References listed on IDEAS

    as
    1. Sun, Xiaojun & Yao, Chong & Song, Enzhe & Liu, Zhijiang & Ke, Yun & Ding, Shunliang, 2023. "Novel enhancement of energy distribution for marine hybrid propulsion systems by an advanced variable weight decision model predictive control," Energy, Elsevier, vol. 274(C).
    2. Acanfora, Maria & Balsamo, Flavio & Fantauzzi, Maurizio & Lauria, Davide & Proto, Daniela, 2023. "Design of an electrical energy storage system for hybrid diesel electric ship propulsion aimed at load levelling in irregular wave conditions," Applied Energy, Elsevier, vol. 350(C).
    3. 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).
    4. Sun, Xiaojun & Yao, Chong & Song, Enzhe & Yang, Qidong & Yang, Xuchang, 2022. "Optimal control of transient processes in marine hybrid propulsion systems: Modeling, optimization and performance enhancement," Applied Energy, Elsevier, vol. 321(C).
    Full references (including those not matched with items on IDEAS)

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