Tobias Brudermüller

Tobias Brudermüller

Tobias Brudermüller

Student / Programme Doctorate at D-MTEC

ETH Zürich

Professur Informationsmanagement

WEV G 218

Weinbergstr. 56/58

8092 Zürich

Switzerland

Additional information

Research area

In his work, Tobias Brudermüller exploits smart meter data and contributes to the area of optimizing residential heat pumps in operation. Heat pumps play a crucial role in decarbonizing the building sector. However, many heat pumps suffer from a significant electricity uptake because they operate below the manufacturer's stated energy efficiency. A large number of installed systems has no network connectivity, and even latest models lack an interface for a unified service. Thus, high energetic and monetary optimization potential remains hidden and is lost. The large-scale roll-out of smart electricity meters constitutes a remedy in this respect. Utility companies already now collect large amounts of electricity consumption data on household-level. The research aims to increase heat pumps' efficiency in a scalable manner, which is a key factor for the decarbonization of the heating sector and the success of the energy transition. 

 

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Tobias Brudermüller holds a master’s degree from RWTH Aachen University (M.Sc. Electrical Engineering, Information Technology & Computer Engineering) and two bachelor’s degrees from the Technical University of Darmstadt (B.Sc. Electrical Engineering & Information Technology // B. Sc. Industrial Engineering & Business Administration). He is specialized in deep learning, machine learning and data science in general. 

Additionally, Tobias Brudermüller was a visiting researcher at Yale University for half a year, founded a startup in the area of augmented reality and has worked for and with multiple companies (GoogleAI, Daimler, SAP, and Merck). Therefore, Tobias Brudermüller has gained practical experience in multiple different projects. These reached from stem cell detection and classification on images of stained bone marrow, or combining machine learning and logic synthesis for computer chip design to environment perception of autonomous vehicles, or building applications in the area of business intelligence. 

Publications

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