Machine Learning for Predicting the Energy Consumption of Catenary Passenger Trains
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2022-07-11
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en
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Abstract
NS (Dutch Railways) wants to reduce the traction energy used by the catenary passenger trains. One step
towards this goal is providing feedback to the train drivers on the level of eco-driving of their journeys.
Creating a baseline for the amount of consumed energy that adapts to the circumstances of a train trip would
make comparisons realistic. Five machine learning models (linear regression, support vector regression,
decision tree, random forest regression (RFR), extreme gradient boosting) are trained to predict the average
energy consumption of train trips (kWh/km) from external factors which the train drivers do not have control
over, creating a dynamic baseline for feedback on eco-driving. The best model is RFR (R2 = 0.91). Furthermore,
four feature importance analysismethods (ablation, permutation, individual conditional expectation, Gini
importance) are conducted to rank the input features by importance. Features regarding details about the
rolling stock, trajectory, temperature and date are important for predicting the energy consumption.
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Faculteit der Sociale Wetenschappen