Data-driven understanding

The aim of my research is to obtain a better understanding of complex systems, in particular complex energy systems, such as the power grid, and thereby support the energy transition. 

In my group “Data-driven analysis of complex systems (DRACOS)”, we combine exploratory data analysis, physical modeling and machine learning methods. One focus is on the interpretability of the models: Instead of “black box” predictions, transparent models are being developed, e.g interpretable machine learning or explainable artificial intelligence (XAI). 

For example, when forecasting the dynamics of the power grid frequency or the consumption of a household, the algorithm should explain which external factors, such as the feed-in of photovoltaic systems, the current electricity price or the time of day, are relevant for its prediction. Such transparency then enables synergies from machine and human models: Are the results obtained by the machine consistent with human models? Where is the machine performing better than the human? What can we learn from the machine to improve upon human-made models? 

graph showing XAI applied in energy systems

Understanding energy systems with eXplainable AI (XAI)

Numerous factors influence the dynamics of energy systems. Complementing human-made models, I pursue the idea of machine-made models, e.g. to predict the power grid stability. However, I pay special attention to the transparency of these models, i.e. the goal is to obtain eXplainable artificial intelligence (XAI), either with post-hoc explanations or with inherently interpretable models.

Read more about XAI applied to power grids in Patterns, ACM e-energy or Energy and AI

Combining physical insights and machine learning

Power systems are experiencing rapid stochastic fluctuations and slower effects of scheduling. Human-designed models and machine- and thereby data-based methods are two complementary approaches to describe these systems. In my research, I integrate domain knowledge into the design of artificial intelligence systems, e.g. training a neural network combined with a stochastic model.

Read more in PRX Energy 2023

splitting frequency dynamics into stochastic and deterministic components

Modelling of driven and stochastic energy systems

Power systems are intrinsically driven by deterministic changes, e.g. the load increases in the morning and then decreases during the late evening. In addition, short-term stochastic changes introduce fluctuations, e.g. when demand fluctuates on a short time scale or (volatile renewable) generation is not constant. I wish to quantify, understand and emulate such driven and stochastic systems. 

Read more in IEEE Access or IEEE PowerTech 2023

Information for potential applicants

I am always open to applications. Please include why you want to work with me and also consider applying for external funding as my group's funding is limited.

Funding options