5 - How Can Energy Transition Impact Switzerland’s Imbalance Evolution?
Switzerland’s power system is entering a new era. The rapid growth of solar PV, electric vehicles, heat pumps and batteries is reshaping how electricity is produced, consumed and shifted over time. At the same time, the grid must remain balanced in real time. As the system becomes more weather-dependent, more distributed and more dynamic, anticipating imbalance becomes increasingly challenging for system and market participants. This challenge invites students, researchers, data scientists and energy enthusiasts to explore a key question for the energy transition: How could Switzerland’s imbalance evolve over time as EV, heat pump, PV and battery penetration increase and to what extent can data-driven approaches capture it?
Challenge Owner
Postdate 22.04.2026
Description
Participants will work with historical data and technology-adoption trends to what extent we can develop data-driven and scenario-based models of future imbalance behavior in Switzerland. Machine learning, forecasting and energy-system modeling are welcome, but they are only examples of possible tools. The objective is to deliver a credible prototype that links electrification pathways, weather uncertainty and forecasting error to future system imbalance. Possible angles include:
- Estimating future adoption trajectories for EVs, PV, heat pumps and batteries from historical data
- Building scenario-based models for future imbalance volume using forecasted inputs
- A first estimate of how imbalance could evolve under these scenarios
- Identifying which variables most strongly explain changes in imbalance behavior
- Bonus: testing simple statistical baselines against more advanced machine-learning approaches.
Impact
- Work on a high-impact real-world problem at the heart of the energy transition.
- Apply data science and machine learning to a system where uncertainty has real operational and economic consequences.
- Learn how weather, consumption, renewable generation, electrification and flexibility interact in the power system.
- Build future-proof skills across energy analytics, forecasting, flexibility, market design and grid digitalization.
- Get familiar with the complex world of electricity balancing, where better prediction can reduce costs, improve system stability and unlock flexibility.
- Collaborate across disciplines such as data science, power systems, optimization, economics and energy policy.
Data Set
Swiss zone imbalance by Swissgrid Electrical technology penetration in Swiss market, by sector.
Needed Skills
- Data mining and machine learning
- Time-series forecasting
- Weather-driven uncertainty
- Energy balancing mechanism