A Novel Hybrid Fuel Consumption Prediction Model for Ocean-Going Container Ships Based on Sensor Data

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

doi:10.3390/jmse9040449

Cited by 23 publications

(8 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When considering the influence of multiple factors as inputs, the random forest (RF) model exhibited poorer performance. Therefore, Hu et al [72] proposed a hybrid model consisting of RF, XGBoosting, and multiple linear regression (MLR) methods to predict ship fuel consumption. Compared to using a single model, this hybrid model achieved smaller error values.…”

Section: Rf Modelmentioning

confidence: 99%

“…Yang et al[18] considered the influence of vehicle factors, environmental factors, and driving behavior factors on fuel consumption, and the MAE of the random forest fuel consumption prediction model was 0.63, which greatly improved the generalization level of the model.When considering the influence of multiple factors as inputs, the random forest (RF) model exhibited poorer performance. Therefore, Hu et al[72] proposed a hybrid model consisting of RF, XGBoosting, and multiple linear regression (MLR) methods to predict ship fuel consumption. Compared to using a single model, this hybrid model achieved smaller error values.…”

mentioning

confidence: 99%

See 1 more Smart Citation

A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption

Zhao

Hou

et al. 2023

Energies

View full text Add to dashboard Cite

Accurately and efficiently predicting the fuel consumption of vehicles is the key to improving their fuel economy. This paper provides a comprehensive review of data-driven fuel consumption prediction models. Firstly, by classifying and summarizing relevant data that affect fuel consumption, it was pointed out that commonly used data currently involve three aspects: vehicle performance, driving behavior, and driving environment. Then, from the model structure, the predictive energy and the characteristics of the traditional machine learning model (support vector machine, random forest), the neural network model (artificial neural network and deep neural network), and this paper point out that: (1) the prediction model of fuel consumption based on neural networks has a higher data processing ability, higher training speed, and stable prediction ability; (2) by combining the advantages of different models to build a hybrid model for fuel consumption prediction, the prediction accuracy of fuel consumption can be greatly improved; (3) when comparing the relevant indicts, both the neural network method and the hybrid model consistently exhibit a coefficient of determination above 0.90 and a root mean square error below 0.40. Finally, the summary and prospect analysis are given based on various models’ predictive performance and application status.

show abstract

Section: Rf Modelmentioning

confidence: 99%

mentioning

confidence: 99%

A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption

Zhao

Hou

et al. 2023

Energies

View full text Add to dashboard Cite

show abstract

“…A similar approach was proposed by [35], where the authors used sensor data collected from an ocean-going container ship. The first-level layer is composed of multiple base-models, namely ET, RF, and XGB, while the second-level layer is a meta model, i.e., multiple linear regression (MLR).…”

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

“…Furthermore, the literature on container logistics transportation in recent years is sorted out. Hu et al (2021) [40] studied the mixed fuel consumption prediction model of ocean-going container ships by using sensor data and estimated the fuel cost of container ship transportation. The results indicate that fuel consumption data of container fleets should be collected in the future.…”

Section: Transportation Costmentioning

confidence: 99%

Prediction and Analysis of Container Terminal Logistics Arrival Time Based on Simulation Interactive Modeling: A Case Study of Ningbo Port

Wang

Bai

2023

Mathematics

View full text Add to dashboard Cite

This study is a driving analysis of the transfer data of container terminals based on simulation interactive modeling technology. In the context of a container yard, a model was established to analyze and predict the arrival time and influencing factors of container transportation through the data from the control center of the yard. The economic benefit index in the index system was determined through expert consultation, the automatic terminal can be obtained by acquiring the actual operating parameters of the terminal, and the terminal to be built can be acquired mainly through simulation modeling. Therefore, when determining the design scheme before constructing the automated container terminal, a terminal simulation model needs to be established that meets the requirements of loading and unloading operations and terminal production operations. In addition, an automated container terminal simulation model needs to be implemented to verify the feasibility of the evaluation model. The results reveal that the accuracy of the current prediction model is still limited—the highest accuracy is only 72%, whether there are continuous or discrete variables, traffic or weather variables. Moreover, the study denotes that the relationship between weather and specific time factors and the arrival time of containers is weak, even negligible. This study provides guidance and decision-making support for the construction of automated terminals.

show abstract

A Novel Hybrid Fuel Consumption Prediction Model for Ocean-Going Container Ships Based on Sensor Data

Cited by 23 publications

References 54 publications

A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption

A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption

A Multitask Learning Framework for Predicting Ship Fuel Oil Consumption

Prediction and Analysis of Container Terminal Logistics Arrival Time Based on Simulation Interactive Modeling: A Case Study of Ningbo Port

Contact Info

Product

Resources

About