Vita

Since his Master in Information Systems at the Humboldt University of Berlin in 2014, Marcus Voss is a Ph.D. student in Energy Informatics and research assistant at the DAI-Labor. There he has coordinated and worked in several research projects investigating how digitization and AI can support the energy transition, e.g., by modeling, forecasting, and optimizing different demand-side processes such as electric vehicles, building and household loads, and renewable generation. In his doctoral research, he analyzes low voltage-level smart meter data using non-Euclidean distance measures with applications in load forecasting and load profile clustering. Currently, he coordinates the Application Center "Smart Energy Systems" and is acting Head of Competence Center "Self-Adaptive Systems".

Research fields

• 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

Skills

• Load Forecasting
• Machine Learning
• Data Science
• Optimization and Simulation

Academic career

• M.Sc. at Humboldt University of Berlin
• Visiting Researcher at OpenUniversity Knowledge Media Institute
• ERASMUS at University of Copenhagen
• B.Sc. at Hochschule für Wirtschaft und Recht

Lectures

• PJ Smart Energy Systems
• PJ Application System Project
• Bachelor and Master Theses in Machine Learning for the Energy System