For railway operators like the National Express Rail GmbH, ordering the correct amount of energy in advance will reduce costs significantly. However, predicting power consumption is difficult since consumption varies due to delays of trains, holidays, weather events and unscheduled rides. By incorporating internal and external data sources on these subjects, we developed a machine learning solution to predict the energy needs of the National Express train fleet. 

The talk will illustrate the challenges of choosing the correct machine learning techniques for this specific application. Furthermore, we will discuss how risk evaluation plays a crucial role in the business evaluation of our forecast.