A two-step strategy for fuel consumption prediction and optimization of ocean-going ships

Hu, Zhihui; Ta, Zhou; Rong, Zhen; Jin, Y.; Li, Xiaohe; Osman, Mohd Tarmizi

doi:10.1016/j.oceaneng.2022.110904

Cited by 25 publications

(15 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors in [34] used data of two real-world voyages from bulk carrier and introduced a hybrid model for predicting fuel consumption. Specifically, the introduced hybrid model is based on stacking theory consisting of two-level layers.…”

Section: Related Work a Fuel Oil Consumption Prediction Taskmentioning

confidence: 99%

A Multitask Learning Framework for Predicting Ship Fuel Oil Consumption

Ilias,

Kapsalis,

Mouzakitis

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Predicting the ship fuel consumption constitutes a prerequisite for speed, trim, and voyage optimization. In spite of the rise of deep learning and transformers in many domains, research works train shallow machine learning (ML) algorithms for predicting ship fuel oil consumption (FOC). Although the auxiliary machinery is in support of the main propulsion engines and the emissions from ships' auxiliary engines contribute to the environmental pollution, most existing research initiatives train ML algorithms for predicting only the main engine FOC. Additionally, all the existing research initiatives use the mean squared error (MSE) as the loss function. However, recent studies have shown that neural network models tend to replicate the last observed value of the time series, thus limiting their applicability to real-world data.To address these limitations, this is the first study proposing transformer-based approaches and a multitask learning (MTL) framework. Firstly, the authors introduce Single-Task learning (STL) models consisting of BiLSTMs and MultiHead Self-Attention for predicting the main and auxiliary engine FOC. Secondly, the authors introduce the first MTL setting, which predicts the main and auxiliary engine FOC simultaneously allowing one task to inform the other. A loss function is introduced, which includes a regularization term for penalizing the replication of previously seen values. The authors evaluate the proposed approaches using data from three fishing ships and compare these approaches with traditional ML algorithms. Extensive experiments show that the introduced MTL models can improve the R 2 score, mean bias error, root mean squared error, and mean absolute error in comparison with shallow ML algorithms.

show abstract

Section: Related Work a Fuel Oil Consumption Prediction Taskmentioning

confidence: 99%

A Multitask Learning Framework for Predicting Ship Fuel Oil Consumption

Ilias,

Kapsalis,

Mouzakitis

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Data collection and processing operations are carried out in the first step, and trim optimization is suggested in the second. Furthermore, trim optimization has been claimed to reduce carbon emissions [32]. According to Vettor and Soares, depending on the route, weather conditions would affect sea wave conditions, which would affect fuel consumption.…”

Section: Related Workmentioning

confidence: 99%

Data-Driven Approach for Estimating Power and Fuel Consumption of Ship: A Case of Container Vessel

et al. 2022

View full text Add to dashboard Cite

In recent years, shipborne emissions have become a growing environmental threat. The International Maritime Organization has implemented various rules and regulations to resolve this concern. The Ship Energy Efficiency Management Plan, Energy Efficiency Design Index, and Energy Efficiency Operational Indicator are examples of guidelines that increase energy efficiency and reduce shipborne emissions. The main engine shaft power (MESP) and fuel consumption (FC) are the critical components used in ship energy efficiency calculations. Errors in ship energy efficiency calculation methodologies are also caused by misinterpretation of these values. This study aims to predict the MESP and FC of a container ship with the help of data-driven methodologies utilizing actual voyage data to assist in the calculation process of the ship’s energy efficiency indexes appropriately. The algorithms’ prediction success was measured using the RMSE, MAE, and R2 error metrics. When the simulation results were analyzed, the Deep Neural Network and Bayes algorithms predicted MESP best with 0.000001 and 0.000002 RMSE, 0.000987 and 0.000991 MAE, and 0.999999 R2, respectively, while the Multiple-Linear Regression and Kernel Ridge algorithms estimated FC best with 0.000208 and 0.000216 RMSE, 0.001375 and 0.001471 MAE, and 0.999999 R2, respectively.

show abstract

“…Subsequently, a data-driven trim optimization model is established in this section. In a specifc marine environment, for a given speed and draft, there is always a corresponding trim value to minimize the energy consumption per unit time of the ship under the current working conditions, and this value is the optimal trim value [32]. Terefore, combined with the established prediction model of the ship's energy consumption and enumeration method, the trim value corresponding to the minimum value of ship energy consumption under specifc navigation conditions, the optimal trim value, is mined.…”

Section: Trim Optimizationmentioning

confidence: 99%

“…In addition, the use of data-driven methods for trim optimization can efectively consider complex marine environments. Du et al [26] and Hu et al [32] developed trim optimization models to obtain energy savings based on the energy consumption data of oceangoing ships and data-driven methods.…”

Section: Introductionmentioning

confidence: 99%

Energy Consumption Optimization of Inland Sea Ships Based on Operation Data and Ensemble Learning

Gao

Chi

2022

Mathematical Problems in Engineering

Self Cite

View full text Add to dashboard Cite

Global warming, environmental pollution, and the soaring cost of energy consumption for ships have drawn the attention of the International Maritime Organization and the shipping industry. By reducing the energy consumption of ships, the greenhouse gas emissions and operating costs of ships can be effectively reduced simultaneously. However, current research on the ship energy consumption optimization based on operating mode is mainly focused on route and speed optimization and less on trim optimization, but ship trim is also an important factor affecting energy consumption. Therefore, this study proposed a ship trim optimization method based on operational data and ensemble learning to achieve energy savings and emission reductions for inland sea ships. First, data processing and feature selection of operational data from an inland ro-ro passenger ship were undertaken. Second, the energy consumption prediction models were established based on ensemble learning. Finally, the trim optimization model was developed by combining the energy consumption model with the best prediction performance and enumeration method. Experimental results show that compared with linear regression, neural networks, and support vector machines, ensemble learning methods have better prediction performance in energy consumption modeling, and the extra tree (ET) model has the best prediction performance. With the trim optimization, the energy consumption and carbon emissions of a ro-ro passenger ship can be reduced by 1.4641%, which is conducive to the green and low-carbon navigation of ships.

show abstract

A two-step strategy for fuel consumption prediction and optimization of ocean-going ships

Cited by 25 publications

References 41 publications

A Multitask Learning Framework for Predicting Ship Fuel Oil Consumption

A Multitask Learning Framework for Predicting Ship Fuel Oil Consumption

Data-Driven Approach for Estimating Power and Fuel Consumption of Ship: A Case of Container Vessel

Energy Consumption Optimization of Inland Sea Ships Based on Operation Data and Ensemble Learning

Contact Info

Product

Resources

About