Predicting VLCC fuel consumption with machine learning using operationally available sensor data. (1st January 2022) - Papandreou, Christos; Ziakopoulos, Apostolos
Abstract: In the maritime industry, more accurate predictions of fuel oil consumption (FOC) could yield multidimensional results including more precise bunker calculations, emission reductions, more informed planning and limiting operational costs. However, models often require sophisticated data that may be partially unavailable to operators beforehand. The present research aims to develop accurate main engine FOC forecasting models that utilize exclusively data from sensors and simple weather data readily available in operational practice. Commonly available sensor data from a Very Large Crude Oil Carrier (VLCC) were used, comprising speed through water, relative wind direction, relative wind speed, mean draft, trim, days since last drydock and laden or ballast vessel state. Multivariate Polynomial Regression (MPR), Artificial Neural Networks (ANNs) and eXtreme Gradient Boosting (XGBoost) regression models were developed and evaluated based on their predictive accuracy for VLCC FOC. Results indicated that XGBoost had the best performance, yielding predictions within 5% of the true value more than 86% of the total cases, followed by MPR and ANN. In addition, accurate aggregate FOC forecasting was conducted with XGBoost for a laden voyage and a ballast voyage of a VLCC. Highlights: Fuel Oil Consumption of a VLCC is modelled using widely available operational data. Polynomial regression, Neural Networks & XGBoost models were trained on VLCC data. XGBoost predicted FOC values within 5% of the true value more than 86% of cases. Successful voyage forecasts were conducted for laden & ballast voyages. Data from a second VLCC confirmed the modelling results of the first vessel.