Rethinking Energy Demand Forecasting with Quantum Machine Learning

​[SAVE THE DATE] Join us on August 20 at 1:00-2:00 p.m. ET | 7:00-8:00 p.m. CET

​Accurate energy demand forecasting is essential for grid stability, renewable integration, and operational planning, yet growing system complexity and correlated consumption patterns are pushing classical approaches to their limits. In partnership with E.ON, WISER explored how hybrid quantum-classical machine learning methods can improve energy demand forecasting by modeling temporal dynamics and cross-customer relationships in new ways.

​In this session, we will examine energy demand forecasting as a high-impact use case for Quantum Machine Learning, highlighting how these approaches were designed, benchmarked, and evaluated for practical relevance. The discussion will offer both technical and strategic insight into where QML stands today, what it can already contribute to forecasting challenges, and how it may support the next generation of energy analytics.

​In this session, you will

​●        See how WISER and E.ON developed hybrid QML approaches for energy demand forecasting.

​●        Understand how model design choices affect forecasting accuracy, scalability, and real-world applicability.

​●        Explore how quantum-enhanced forecasting compares with classical methods in complex energy environments.

​●        Gain a clear view of where Quantum Machine Learning stands today and its path toward adoption in the energy sector.