Using future data to better predict Switzerland's rail energy demand
In collaboration with SBB, EPFL researchers have developed an AI model that improves next-day electricity-demand forecasts across Switzerland’s rail network, reducing major prediction errors by up to 80%.
In collaboration with SBB, EPFL researchers have developed an AI model that improves next-day electricity-demand forecasts across Switzerland’s rail network, reducing major prediction errors by up to 80%.
Every day, Switzerland’s rail network must anticipate how much electricity it will need to keep trains running. Yet accurate forecasts are challenging as demand depends on many interacting factors, including fluctuating passenger flows, weather conditions, and constantly changing operations. Large prediction errors can lead to significant operational risks and costs. Some studies suggest that even a 1% reduction in forecasting error could translate into annual savings of around one million Swiss francs.
To tackle this problem, a team of researchers at Intelligent Maintenance and Operations Systems Laboratory at EPFL, led by tenure-track assistant professor Olga Fink and PhD student Raffael Pascal Theiler, developed a new AI forecasting model in collaboration with SBB and partners at Empa, ETH Zurich, and MIT. Their approach combines historical railway data with contextual information such as train timetables, operational planning data, and weather forecasts.
The results, published in Energy Reports, show that incorporating this additional information reduces the average prediction error by 26.6% compared to the model without this information. The improvement is even more striking on unusual days, when train operations or passenger flows differ significantly from the past. In this case, large forecasting errors are reduced by about 80%. “Because the model has information about planned future operations, it is better prepared for unusual days,” says Fink. The approach could help make Swiss rail operations more efficient by improving energy management, while reducing costs and environmental impact.
Applications beyond the rails
Railway operation is just one example of how future contextual information can help improve energy demand forecasting. The researchers have already demonstrated the approach in building energy systems, where occupancy schedules and planned activities improve forecasts of energy demand.
Author: Hector Garcia Morales