Marcus Voss

Marcus Voss

Intelligence Architect and AI Expert, PhD Student

birds on mars

TU Berlin (DAI-Lab)

Climate Change AI

CorrelAid

Biography

I am passionate about using machine learning, AI, and data-driven methods to support the energy transition and climate change mitigation and adaptation.

I am an Intelligence Architect and AI Expert at bird on mars, where I enable companies to make use of their data.

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 coordinated the research group Smart Energy Systems, where I was 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.

Interests
  • Smart Meter Data Analytics in Low-Voltage Systems
  • Demand-Side Load Forecasting using Machine Learning
  • E-Mobility and Buildings as Flexibility in the Smart Grid
Education
  • PhD in Computer Science, 2021 (planned)

    TU Berlin

  • M.Sc. in Information Systems, 2014

    Humboldt University of Berlin

  • B.Sc. in Information Systems, 2011

    HWR Berlin

Recent & Upcoming Talks

Recent Publications

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(2021). Probabilistic Short-Term Low-Voltage Load Forecasting using Bernstein-Polynomial Normalizing Flows. Workshop Tackling Climate Change with Machine Learning at ICML.

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(2021). DIN SPEC 91410-2:2021-05: Energieflexibilität – Teil 2: Identifizierung und Bewertung von Flexibilität in Gebäuden und Quartieren. Beuth Verlag.

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(2020). Integration of Building Inertia Thermal Energy Storage into Smart Grid Control. In SEST 2020.

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(2020). Sector-Coupled District Energy Management with Heating and Bi-Directional EV-Charging. In IEEE PowerTech 2021.

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