Predicting ship fuel consumption using artificial intelligence based on real-time data
This paper examines the significance of accurate predictions of ship fuel consumption, highlighting its role in cost reduction and mitigating carbon dioxide emissions. While container ships have been extensively researched, there has been a noticeable lack of studies done on passenger ferries. He...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/176413 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | This paper examines the significance of accurate predictions of ship fuel
consumption, highlighting its role in cost reduction and mitigating carbon dioxide
emissions. While container ships have been extensively researched, there has been a
noticeable lack of studies done on passenger ferries. Hence, this study conducts a
comparative analysis of various Artificial Intelligence methods for predicting fuel
consumption in passenger ferries. The analysis includes outlier detection using KNearest Neighbours, and employs models such as Multiple Linear Regression, Lasso
Regression, XGBoost, and Artificial Neural Network in making the predictions.
Performance evaluation metrics, including coefficients of determination and root
mean squared error, are utilized to assess the model's performance. The findings
reveal that XGBoost and Artificial Neural Network achieve the highest accuracy in
predicting fuel consumption. |
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