Data analytics for fuel consumption management in maritime transportation: Status and perspectives

Yan, Ran; Wang, Shuaian; Psaraftis, Harilaos N.

doi:10.1016/j.tre.2021.102489

Cited by 67 publications

(38 citation statements)

References 113 publications

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“…Another recent review of fuel consumption prediction (which also addresses optimization) is carried out by Yan et al (2021a). In line with the findings by Yan et al (2021b), this review indicates that machine learning has not been applied extensively for port vessel EIs, showing potential in its further exploration.…”

Section: Literature Reviewmentioning

confidence: 70%

Tiered prediction models for port vessel emissions inventories

Cammin

Voß

2022

Flex Serv Manuf J

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Albeit its importance, a large number of port authorities do not provide continuous or publicly available air emissions inventories (EIs) and thereby obscure the emissions contribution of ports. This is caused by, e.g., the economic effort generated by obtaining data. Therefore, the performance of abatement measures is not monitored and projected, which is specifically disadvantageous concerning top contributors such as container ships. To mitigate this issue, in this paper we propose port vessel EI prediction models by exploring the combination of different machine-learning algorithms, data from the one-off application of an activity-based bottom-up methodology and vessel-characteristics data. The results for this specific case show that prediction models enable acceptable trade-offs between the prediction performance and data requirements, promoting the creation of EIs.

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Section: Literature Reviewmentioning

confidence: 70%

Tiered prediction models for port vessel emissions inventories

Cammin

Voß

2022

Flex Serv Manuf J

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“…But, despite the strength of WBMs, they have some obvious disadvantages: WBMs model's parameters are indeed explainable, but many assumptions must be made before the constructions of the model, and the prior assumptions made can have a large impact on the model's performance and whether it can be applied on real situations. Besides, in order to fnd the parameters needed, ship structures, and building information, complete sail activities must be complete and fxed, i.e., missing data cannot be applied to such model, and no randomness can be included to allow data uncertainties [15]. Terefore, though the WBMs can be a relatively accurate result, the performance is strict to "fxed" and "absolute" conditions, which make it hard to be applied to real-world shipping operations.…”

Section: Introductionmentioning

confidence: 99%

Container Ship Carbon and Fuel Estimation in Voyages Utilizing Meteorological Data with Data Fusion and Machine Learning Techniques

Ren

Wang

Liu

et al. 2022

Mathematical Problems in Engineering

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The International Maritime Organization (IMO) had made effort to reduce the ship’s energy consumption and carbon emission by optimizing the ship’s operational measures such as speed and weather routing. However, existing fuel consumption models were relatively simple without considering the quantified effect of weather conditions. In this paper, a knowledge-based ridge regression-based algorithm is presented for enabling automated fuel consumption estimation under varying weather conditions during voyages. Wind speed, wave height, ship speed, draught, AIS segment distance, and ship’s heading (HDG) are used as input to predict the fuel consumption value from the MRV report. In this work, 3 types of models are tested: AIS-based model, MRV-based model, and MRV-based normalized model. In AIS based model, weather conditions are divided into nine categories based on wind speed, wave height, and wind directions then trained separately. In MRV-based mode, the daily weather condition was used, and the MRV-normalized model used the normalized daily weather data. The proposed ridge regression models (11 models total) were tested with 4 container ships for a period of one year, and the result shows that compared to real fuel consumption, MRV-based model could achieve the best result with an average error less than 3% comparing to real MRV report.

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“…Marine transportation is efficient because more than 80% of goods delivered to various countries are shipped [1]. e efficiency of maritime transportation can be effectively improved by implementing ship speed control and route planning [2][3][4]. In maritime transportation, speed control is vital to ensure the safe navigation of the vessel to arrive at the destination port.…”

Section: Introductionmentioning

confidence: 99%

Theory and Application of Vessel Speed Dynamic Control considering Safety and Environmental Factors

et al. 2022

Journal of Advanced Transportation

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The implementation of ship speed control is extremely important in the shipping industry. It is affected by various factors, such as water depth, obstacles, and environmental factors. Traditional speed control methods only consider geographical constraints, which is difficult to achieve the goal of safe navigation and maritime traffic efficiency simultaneously. Accordingly, a two-stage speed dynamic control model is proposed in this study. In the first stage, certain safety navigation factors, including obstacles, sea environment conditions, and limit of estimated time of arrival to destination port, are considered. In the second stage, the speed dynamic control model considering safety and environmental factors is established by combining multisource data and particle swarm optimisation algorithm. The model’s superiority and advantage are validated by experiments conducted on an ocean-going ship. The experimental results show that the proposed dynamic speed control model can reduce the ship’s fuel consumption and improve energy efficiency while ensuring the safety navigation. The study is anticipated to be used as a reference for speed dynamic control.

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Data analytics for fuel consumption management in maritime transportation: Status and perspectives

Cited by 67 publications

References 113 publications

Tiered prediction models for port vessel emissions inventories

Tiered prediction models for port vessel emissions inventories

Container Ship Carbon and Fuel Estimation in Voyages Utilizing Meteorological Data with Data Fusion and Machine Learning Techniques

Theory and Application of Vessel Speed Dynamic Control considering Safety and Environmental Factors

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