Lukas Franken



Enrichment Student

Cohort year


Partner Institution


Lukas is a PhD student working at the intersection of machine learning and energy systems. He started his doctoral studies at the University of Edinburgh in Summer 2022 after completing a Master's degree in Physics at the University of Cologne, Germany. During his Master's, Lukas worked on quantum computing and was active as a student researcher at the Fraunhofer Institute IAIS, where he worked on the theory of deep learning.

Research interests

Lukas is mainly but not exclusively interested in energy systems and long-term storage technologies. In that context, Lukas applies machine learning in various capacities: First, some of the most affordable and efficient storage technologies are very slow to release their energy. Hence, they are commonly used as part of district heating systems where being able to predict demand can be greatly beneficial in their operation. It turns out that such a district's design affects how easy it is to predict that demand.

Another aspect, in which machine learning is relevant in energy systems comes with its potential to reduce the computational overhead of energy system simulations. Due to their complexity, such simulations are usually run on software that takes a long time to execute and acts as a black-box. However, it is common that between simulation runs, only a small number of input parameters are changed while only a single or a small number of output variables are of interest. Hence, given a set of simulation runs, a machine learning model can learn the relationship between input parameters and output variables, replace the software in future runs and thereby greatly reduce runtime.