Prediction of Fuel Consumption for Enroute Ship Based on Machine Learning
Due to the hike in fuel price and environmental awareness by the International Maritime Organization, more attention has been given in order to optimize the fuel consumption of ships. The capability to predict the fuel consumption of ships plays a significant role in the optimization process. To dat...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8790676/ |
| _version_ | 1818910135871864832 |
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| author | Zhihui Hu Yongxin Jin Qinyou Hu Sukanta Sen Tianrui Zhou Mohd Tarmizi Osman |
| author_facet | Zhihui Hu Yongxin Jin Qinyou Hu Sukanta Sen Tianrui Zhou Mohd Tarmizi Osman |
| author_sort | Zhihui Hu |
| collection | DOAJ |
| description | Due to the hike in fuel price and environmental awareness by the International Maritime Organization, more attention has been given in order to optimize the fuel consumption of ships. The capability to predict the fuel consumption of ships plays a significant role in the optimization process. To date, most research on predicting ship fuel consumption did not consider marine environmental factors such as wind, wave, current, and etc. Furthermore, traditional statistical methods on predicting ship fuel consumption have low accuracy. In this paper, two different sets of data showing the fuel consumption of a voyage ship with and without the influence of marine environmental factors were obtained. The Back-Propagation Neural Network (BPNN) and Gaussian Process Regression (GPR) techniques in machine learning were used to train and predict the two datasets. Thereafter, the predictive performance of these two techniques was compared and analyzed. Results showed that both techniques were able to accurately predict the ship fuel consumption, especially on the dataset with the influence of marine environmental factors. Quantitatively, the mean prediction accuracy for GPR (mean R<sup>2</sup> = 0.9887) is slightly higher than BPNN (mean R<sup>2</sup> = 0.9817). However, GPR requires longer runtime (mean T = 2236.4 s) compared to BPNN (mean T = 14.7 s). Due to the longer runtime, GPR is less preferable for online and real-time prediction of enroute ship fuel consumption. The ship real-time fuel consumption data can be accurately predicted by machine learning, which will be beneficial to achieve the goal of ship fuel consumption optimization and greenhouse gas emission reduction in the future. |
| first_indexed | 2024-12-19T22:38:00Z |
| format | Article |
| id | doaj.art-b554bb640203421e907a7ebee2ac1a91 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T22:38:00Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b554bb640203421e907a7ebee2ac1a912022-12-21T20:03:08ZengIEEEIEEE Access2169-35362019-01-01711949711950510.1109/ACCESS.2019.29336308790676Prediction of Fuel Consumption for Enroute Ship Based on Machine LearningZhihui Hu0https://orcid.org/0000-0002-6572-7986Yongxin Jin1Qinyou Hu2Sukanta Sen3Tianrui Zhou4Mohd Tarmizi Osman5Merchant Marine College, Shanghai Maritime University, Shanghai, ChinaMerchant Marine College, Shanghai Maritime University, Shanghai, ChinaMerchant Marine College, Shanghai Maritime University, Shanghai, ChinaMerchant Marine College, Shanghai Maritime University, Shanghai, ChinaMerchant Marine College, Shanghai Maritime University, Shanghai, ChinaMerchant Marine College, Shanghai Maritime University, Shanghai, ChinaDue to the hike in fuel price and environmental awareness by the International Maritime Organization, more attention has been given in order to optimize the fuel consumption of ships. The capability to predict the fuel consumption of ships plays a significant role in the optimization process. To date, most research on predicting ship fuel consumption did not consider marine environmental factors such as wind, wave, current, and etc. Furthermore, traditional statistical methods on predicting ship fuel consumption have low accuracy. In this paper, two different sets of data showing the fuel consumption of a voyage ship with and without the influence of marine environmental factors were obtained. The Back-Propagation Neural Network (BPNN) and Gaussian Process Regression (GPR) techniques in machine learning were used to train and predict the two datasets. Thereafter, the predictive performance of these two techniques was compared and analyzed. Results showed that both techniques were able to accurately predict the ship fuel consumption, especially on the dataset with the influence of marine environmental factors. Quantitatively, the mean prediction accuracy for GPR (mean R<sup>2</sup> = 0.9887) is slightly higher than BPNN (mean R<sup>2</sup> = 0.9817). However, GPR requires longer runtime (mean T = 2236.4 s) compared to BPNN (mean T = 14.7 s). Due to the longer runtime, GPR is less preferable for online and real-time prediction of enroute ship fuel consumption. The ship real-time fuel consumption data can be accurately predicted by machine learning, which will be beneficial to achieve the goal of ship fuel consumption optimization and greenhouse gas emission reduction in the future.https://ieeexplore.ieee.org/document/8790676/Machine learningGaussian process regressionback-propagation neural networkenroute shipfuel consumption prediction |
| spellingShingle | Zhihui Hu Yongxin Jin Qinyou Hu Sukanta Sen Tianrui Zhou Mohd Tarmizi Osman Prediction of Fuel Consumption for Enroute Ship Based on Machine Learning IEEE Access Machine learning Gaussian process regression back-propagation neural network enroute ship fuel consumption prediction |
| title | Prediction of Fuel Consumption for Enroute Ship Based on Machine Learning |
| title_full | Prediction of Fuel Consumption for Enroute Ship Based on Machine Learning |
| title_fullStr | Prediction of Fuel Consumption for Enroute Ship Based on Machine Learning |
| title_full_unstemmed | Prediction of Fuel Consumption for Enroute Ship Based on Machine Learning |
| title_short | Prediction of Fuel Consumption for Enroute Ship Based on Machine Learning |
| title_sort | prediction of fuel consumption for enroute ship based on machine learning |
| topic | Machine learning Gaussian process regression back-propagation neural network enroute ship fuel consumption prediction |
| url | https://ieeexplore.ieee.org/document/8790676/ |
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