Marcus Voss

Marcus Voss

Intelligence Architect and AI Expert, PhD Student

Birds on Mars

TU Berlin (DAI-Lab)

Climate Change AI



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 Birds on Mars, where I enable companies to create value with 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.

  • Sustainable use cases of AI and Machine Learning
  • Applications of AI and ML in energy systems
  • 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). Nachhaltigkeitskriterien für künstliche Intelligenz - Entwicklung eines Kriterien- und Indikatorensets für die Nachhaltigkeitsbewertung von KI-Systemen entlang des Lebenszyklus. IÖW-Schriftenreihe 220/21.

PDF Cite

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


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


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


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