Machine Learning for Energy Systems

Integrating renewable energy sources into the electricity grid comes with new challenges and opportunities. The aim in this area is enhancing the efficiency and functionality of energy systems, ranging from small-scale household infrastructures to large-scale transmission networks. As energy systems represent a web of interdependent components on these multiple scales that are rich in prior knowledge, they present an opportunity to integrate extensive physical understanding into machine learning models.

Main Research Interests:

• Optimisation of Solar Thermal Systems (e.g. Fault Detection)
• Graph Coarsening for Energy System Models
• Probabilistic Forecasting of Energy Demand and Supply

Recent Research Highlight