Browsing by Subject "Continuous Learning"

Accurately predicting a ship’s fuel consumption is essential for an efficient shipping operation. A prediction model has to be regularly retrained to minimize drift between its predictions and the actual consumption of the ship since a ship’s performance is constantly changing because of weather influences and constant hull fouling. Continuous Learning (CL) promises repeated retraining of an ML model while also mitigating catastrophic forgetting. The so-called catastrophic forgetting happens when a model is trained on new data without proper measures to “remind” the model of its previous knowledge. In the context of Ship Performance Prediction, this might be previously encountered weather or performance patterns in certain conditions. This thesis explores the adaptability of CL to set up a production-ready training pipeline to regularly retrain a model that predicts a ship’s fuel consumption.