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Time-Aware Validation of Machine Learning Fuel ConsumptionModels: Evidence from 1 Hz Operational Data, CCGS Sir WilfridLaurier

https://doi.org/10.2139/ssrn.7429773
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Fuel consumption prediction for a passenger ferry using machine learning and in-service data: A comparative study
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Predicting ship fuel consumption using a combination of metocean and on-board data
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An application-oriented testing regime and multi-ship predictive modeling for vessel fuel consumption prediction
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Random Forests
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Ridge Regression: Biased Estimation for Nonorthogonal Problems
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Effect of ship hull form on the resistance penalty from biofouling
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Regularization and Variable Selection Via the Elastic Net
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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
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Regression Shrinkage and Selection Via the Lasso
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Ship Route Design for Avoiding Heavy Weather and Sea Conditions
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Added Resistance of Ships in Waves
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An approximate power prediction method
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Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature
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Ship Hull Fouling Estimation Using Shipboard Measurements, Models for Resistance Components, and Shaft Torque Calculation Using Engine Model
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