I am passionate about using machine learning, AI, and data-driven methods to support the energy transition and climate change mitigation and adaptation.
At the Distributed Artificial Intelligence Laboratory (DAI-Lab), I have coordinated and worked on several research projects investigating how digitization and AI can support the energy transition. Within those projects, I have been modeling, forecasting, and optimizing different demand-side processes such as electric vehicles, smart building- and smart home loads, and renewable generation. I am currently coordinating the research group Smart Energy Systems, where I am responsible for aligning the DAI research for solutions for the energy system. I have co-supervised several Seminar, Bachelor and Master theses and project-based courses, mostly in applied machine learning for energy data, hoping to inspire more students to work in the field.
In my doctoral research, I work on analyzing low voltage-level smart meter data using non-Euclidean distance measures and neural networks with applications in load forecasting and load profile clustering.
I volunteer in Climate Change AI’s content committee to provide resources for researchers and practitioners transitioning into AI and machine learning for climate change mitigation and adaptation.
PhD in Computer Science, 2021 (planned)
M.Sc. in Information Systems, 2014
Humboldt University of Berlin
B.Sc. in Information Systems, 2011
This paper presents a literature review on the topic of Low Voltage (LV) load forecasting. It gives an overview of the approaches, core applications, datasets, trends, and challenges. Suggestions how to facilitate the continued improvement and advancement are given and a set of recommendations toward best practises are provided.
A building’s structural mass does provide inherent thermal storage capabilities. Within this work, a mathematical model of a building inertia thermal energy storage (BITES) is proposed to allow integration into optimized smart grid control for real-world applications.