Sensors installed on a ship return high quality data that can be used for ship bunker fuel efficiency analysis. However, important information about weather and sea conditions the ship sails through, such as waves, sea currents, and sea water temperature, is often absent from sensor data. This study addresses this issue by fusing sensor data and publicly accessible meteorological data, constructing nine datasets accordingly, and experimenting with widely adopted machine learning (ML) models to quantify the relationship between a ship's fuel consumption rate (ton/day, or ton/h) and its voyage-based factors (sailing speed, draft, trim, weather conditions, and sea conditions). The best dataset found reveals the benefits of fusing sensor data and meteorological data for ship fuel consumption rate quantification. The best ML models found are consistent with our previous studies, including Extremely randomized trees (ET), Gradient Tree Boosting (GB) and XGBoost (XG). Given the best dataset from data fusion, their R² values over the training set are 0.999 or 1.000, and their R² values over the test set are all above 0.966. Their fit errors with RMSE values are below 0.75 ton/day, and with MAT below 0.52 ton/day. These promising results are well beyond the requirements of most industry applications for ship fuel efficiency analysis. The applicability of the selected datasets and ML models is also verified in a rolling horizon approach, resulting in a conjecture that a rolling horizon strategy of "5-month training + 1-month test/applicatoin" could work well in practice and sensor data of less than five months could be insufficient to train ML models.

When voyage report data is utilized as the main data source for ship fuel efficiency analysis, its information on weather and sea conditions is often regarded as unreliable. To solve this issue, this study approaches AIS data to obtain the ship's actual detailed geographical positions along its sailing trajectory and then further retrieve the weather and sea condition information from publicly accessible meteorological data sources. These more reliable data about weather and sea conditions the ship sails through is fused into voyage report data in order to improve the accuracy of ship fuel consumption rate models. Eight 8100-TEU to 14,000-TEU containerships from a global shipping company were used in experiments. For each ship, nine datasets were constructed based on data fusion and eleven widely-adopted machine learning models were tested. Experimental results revealed the benefits of fusing voyage report data, AIS data, and meteorological data in improving the fit performances of machine learning models of forecasting ship fuel consumption rate. Over the best datasets, the performances of several decision tree-based models are promising, including Extremely randomized trees (ET), AdaBoost (AB), Gradient Tree Boosting (GB) and XGBoost (XG). With the best datasets, their R² values over the training sets are all above 0.96 and mostly reach the level of 0.99–1.00, while their R² values over the test sets are in the range from 0.75 to 0.90. Fit errors of ET, AB, GB, and XG on daily bunker fuel consumption, measured by RMSE and MAE, are usually between 0.8 and 4.5 ton/day. These results are slightly better than our previous study, which confirms the benefits of adopting the actual geographical positions of the ship recorded by AIS data, compared with the estimated geographical positions derived from the great circle route, in retrieving weather and sea conditions the ship sails through.

The International Maritime Organization has been promoting energy-efficient operational measures to reduce ships' bunker fuel consumption and the accompanying emissions, including speed optimization, trim optimization, weather routing, and the virtual arrival policy. The theoretical foundation of these measures is a model that can accurately forecast a ship's bunker fuel consumption rate according to its sailing speed, displacement/draft, trim, weather conditions, and sea conditions. Voyage report is an important data source for ship fuel efficiency modeling but its information quality on weather and sea conditions is limited by a snapshotting practice with eye inspection. To overcome this issue, this study develops a solution to fuse voyage report data and publicly accessible meteorological data and constructs nine datasets based on this data fusion solution. Eleven widely-adopted machine learning models were tested over these datasets for eight 8100-TEU to 14,000-TEU containerships from a global shipping company. The best datasets found reveal the benefits of fusing voyage report data and meteorological data, as well as the practically acceptable quality of voyage report data. Extremely randomized trees (ET), AdaBoost (AB), Gradient Tree Boosting (GB) and XGBoost (XG) present the best fit and generalization performances. Their R values over the best datasets are all above 0.96 and even reach 0.99 to 1.00 for the training set, and 0.74 to 0.90 for the test set. Their fit errors on daily bunker fuel consumption are usually between 0.5 and 4.0 ton/day. These models have good interpretability in explaining the relative importance of different determinants to a ship's fuel consumption rate.