Navigating Energy Efficiency: A Multifaceted Interpretability of Fuel Oil Consumption Prediction in Cargo Container Vessel Considering the Operational and Environmental Factors
In the maritime industry, optimizing vessel fuel oil consumption is crucial for improving energy efficiency and reducing shipping emissions. However, effectively utilizing operational data to advance performance monitoring and optimization remains a challenge. An XGBoost Regressor model was develope...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/11/11/2165 |
| _version_ | 1797458757173641216 |
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| author | Melia Putri Handayani Hyunju Kim Sangbong Lee Jihwan Lee |
| author_facet | Melia Putri Handayani Hyunju Kim Sangbong Lee Jihwan Lee |
| author_sort | Melia Putri Handayani |
| collection | DOAJ |
| description | In the maritime industry, optimizing vessel fuel oil consumption is crucial for improving energy efficiency and reducing shipping emissions. However, effectively utilizing operational data to advance performance monitoring and optimization remains a challenge. An XGBoost Regressor model was developed using a comprehensive dataset, delivering strong predictive performance (R<sup>2</sup> = 0.95, MAE = 10.78 kg/h). This predictive model considers operational (controllable) and environmental (uncontrollable) variables, offering insights into complex FOC factors. To enhance interpretability, SHAP analysis is employed, revealing ‘Average Draught (Aft and Fore)’ as the key controllable factor and emphasizing ‘Relative Wind Speed’ as the dominant uncontrollable factor impacting vessel FOC. This research extends to further analysis of the extremely high FOC point, identifying patterns in the Strait of Malacca and the South China Sea. These findings provide region-specific insights, guiding energy efficiency improvement, operational strategy refinement, and sea resistance mitigation. In summary, our study introduces a groundbreaking framework leveraging machine learning and SHAP analysis to advance FOC understanding and enhance maritime decision making, contributing significantly to energy efficiency and operational strategies—a substantial contribution to a responsible shipping performance assessment under tightening regulations. |
| first_indexed | 2024-03-09T16:41:46Z |
| format | Article |
| id | doaj.art-2065759aba7c4d67b52af1df8e7d07ce |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-09T16:41:46Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-2065759aba7c4d67b52af1df8e7d07ce2023-11-24T14:50:41ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-11-011111216510.3390/jmse11112165Navigating Energy Efficiency: A Multifaceted Interpretability of Fuel Oil Consumption Prediction in Cargo Container Vessel Considering the Operational and Environmental FactorsMelia Putri Handayani0Hyunju Kim1Sangbong Lee2Jihwan Lee3Department of Industrial and Data Engineering, Major in Industrial Data Science and Engineering, Pukyong National University, Busan 48513, Republic of KoreaKorea Marine Equipment Research Institute, Busan 49111, Republic of KoreaLab021 Shipping Analytics, Busan 48508, Republic of KoreaDepartment of Industrial and Data Engineering, Major in Industrial Data Science and Engineering, Pukyong National University, Busan 48513, Republic of KoreaIn the maritime industry, optimizing vessel fuel oil consumption is crucial for improving energy efficiency and reducing shipping emissions. However, effectively utilizing operational data to advance performance monitoring and optimization remains a challenge. An XGBoost Regressor model was developed using a comprehensive dataset, delivering strong predictive performance (R<sup>2</sup> = 0.95, MAE = 10.78 kg/h). This predictive model considers operational (controllable) and environmental (uncontrollable) variables, offering insights into complex FOC factors. To enhance interpretability, SHAP analysis is employed, revealing ‘Average Draught (Aft and Fore)’ as the key controllable factor and emphasizing ‘Relative Wind Speed’ as the dominant uncontrollable factor impacting vessel FOC. This research extends to further analysis of the extremely high FOC point, identifying patterns in the Strait of Malacca and the South China Sea. These findings provide region-specific insights, guiding energy efficiency improvement, operational strategy refinement, and sea resistance mitigation. In summary, our study introduces a groundbreaking framework leveraging machine learning and SHAP analysis to advance FOC understanding and enhance maritime decision making, contributing significantly to energy efficiency and operational strategies—a substantial contribution to a responsible shipping performance assessment under tightening regulations.https://www.mdpi.com/2077-1312/11/11/2165maritimeship energy efficiencyfuel oil consumption predictionship performance assessmentdata analyticsmachine learning |
| spellingShingle | Melia Putri Handayani Hyunju Kim Sangbong Lee Jihwan Lee Navigating Energy Efficiency: A Multifaceted Interpretability of Fuel Oil Consumption Prediction in Cargo Container Vessel Considering the Operational and Environmental Factors Journal of Marine Science and Engineering maritime ship energy efficiency fuel oil consumption prediction ship performance assessment data analytics machine learning |
| title | Navigating Energy Efficiency: A Multifaceted Interpretability of Fuel Oil Consumption Prediction in Cargo Container Vessel Considering the Operational and Environmental Factors |
| title_full | Navigating Energy Efficiency: A Multifaceted Interpretability of Fuel Oil Consumption Prediction in Cargo Container Vessel Considering the Operational and Environmental Factors |
| title_fullStr | Navigating Energy Efficiency: A Multifaceted Interpretability of Fuel Oil Consumption Prediction in Cargo Container Vessel Considering the Operational and Environmental Factors |
| title_full_unstemmed | Navigating Energy Efficiency: A Multifaceted Interpretability of Fuel Oil Consumption Prediction in Cargo Container Vessel Considering the Operational and Environmental Factors |
| title_short | Navigating Energy Efficiency: A Multifaceted Interpretability of Fuel Oil Consumption Prediction in Cargo Container Vessel Considering the Operational and Environmental Factors |
| title_sort | navigating energy efficiency a multifaceted interpretability of fuel oil consumption prediction in cargo container vessel considering the operational and environmental factors |
| topic | maritime ship energy efficiency fuel oil consumption prediction ship performance assessment data analytics machine learning |
| url | https://www.mdpi.com/2077-1312/11/11/2165 |
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