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

Mohamed Abuella, Hadi Fanaee, Slawomir Nowaczyk, Simon Johansson, Ethan Faghani

TL;DR

The paper addresses energy efficiency in fixed-route short-sea shipping by leveraging time-series analysis to optimize voyage speed. It proposes a data-driven framework that aggregates onboard and external data, defines an efficiency score $Eff_Score$ to rank voyages, and clusters voyages to tailor model training. It compares four time-series models—LSTM, KNN, 1NN-DTW, and HMM—using real-world data from a passenger vessel over 15 months and finds the HMM model delivers the largest energy-efficiency gains, up to about 6% on average, especially when trained on high-efficiency clusters. The results demonstrate practical potential for energy-efficient operations in constrained SSS contexts, aligning with safety and regulatory considerations and offering open-source code for replication.

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.

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

TL;DR

The paper addresses energy efficiency in fixed-route short-sea shipping by leveraging time-series analysis to optimize voyage speed. It proposes a data-driven framework that aggregates onboard and external data, defines an efficiency score to rank voyages, and clusters voyages to tailor model training. It compares four time-series models—LSTM, KNN, 1NN-DTW, and HMM—using real-world data from a passenger vessel over 15 months and finds the HMM model delivers the largest energy-efficiency gains, up to about 6% on average, especially when trained on high-efficiency clusters. The results demonstrate practical potential for energy-efficient operations in constrained SSS contexts, aligning with safety and regulatory considerations and offering open-source code for replication.

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.
Paper Structure (16 sections, 3 equations, 7 figures, 4 tables, 2 algorithms)

This paper contains 16 sections, 3 equations, 7 figures, 4 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Framework of Voyage Optimization
  5. Energy Efficiency Modeling
  6. Time-Series Analysis Models for Voyage Optimization
  7. Long Short-Term Memory (LSTM)
  8. k-Nearest Neighbors (kNN)
  9. 1-Nearest Neighbor with Dynamic Time Warping (1NN-DTW)
  10. Hidden Markov Model (HMM)
  11. Case Study
  12. Data Collection
  13. Data Preparation and Validation
  14. Implementation of the Framework
  15. Results and Discussion
  16. ...and 1 more sections

Figures (7)

  • Figure 1: Framework of vessel voyage optimization.
  • Figure 2: The vessel voyages and the aggregated data projected by the Efficiency Score.
  • Figure 3: Four clusters of voyages based on their efficiency.
  • Figure 4: Vessel's data analytics and standard performance graph for the case study Carlton2018
  • Figure 5: Barplots for statistics of Fuel, Time, and Distance in different route segments
  • ...and 2 more figures