(IN BRIEF) Researchers from EPFL and Empa, in collaboration with SBB, have developed an AI model that improves next-day electricity demand forecasting across Switzerland’s rail network. The model combines historical energy-consumption data with contextual information such as train timetables, operational … Read the full press release →
Posted in Artificial Intelligence (AI), Business, Education, Energy, Gas & Oil, Environment, Infrastructure & Utilities, Science, Switzerland, Technology, Transportation & Logistics
Tagged AI Forecasting Model Helps SBB Improve Energy Management Across Swiss Rail Network, AI model, anomaly detection, artificial intelligence, Building Energy Systems, converter stations, cost reduction, electricity demand forecasting, Empa, energy consumption, Energy Reports, environmental impact, EPFL, EPFL-Led Study Shows Operational Data Can Strengthen Railway Electricity Forecasting, ETH Zurich, historical railway data, Hydropower generation, logistics, Manufacturing, MIT, next-day energy forecasts, Olga Fink, operational planning data, passenger flows, prediction errors, pumped-storage hydropower, Raffael Pascal Theiler, railway energy management, railway power grid, Researchers Use AI to Improve Next-Day Energy Demand Forecasts Across Switzerland’s Railway Network, Sarah Aubort, SBB, substations, Supply Chain Management, Swiss rail system, Switzerland railway network, train timetables, transmission lines, weather forecasts
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