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tbrumue/README.md

Hi there! I'm Tobias Brudermüller. 👋

Currently, I'm a third-year PhD candidate in the Bits to Energy Lab at ETH Zurich. In my current research, I develop machine learning techniques for energy applications to promote sustainability and for the digitalization of the power grid. 🔋🔌💡 One area of focus for my work is analyzing data from smart electricity meters to optimize residential heat pumps in operation. For my research, I currently collaborate with multiple partners: Swiss Federal Office of Energy, Bosch, EKZ, Enerlytica, and Hoval.

General links:

Links to some of my work:

Publication Paper Code
Brudermueller, T., & Kreft, M. (2023). Smart Meter Data Analytics: Practical Use-Cases and Best Practices of Machine Learning Applications for Energy Data in the Residential Sector. Workshop Tackling Climate Change with Machine Learning, International Conference on Learning Representations (ICLR). Link Link
Brudermueller, T., Kreft, M., Fleisch, E., & Staake, T. (2023). Large-scale monitoring of residential heat pump cycling using smart meter data. Applied Energy, 350, 121734. Link Link
Brudermueller, T., Breer, F., & Staake, T. (2023). Disaggregation of Heat Pump Load Profiles From Low-Resolution Smart Meter Data. In Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys '23). Association for Computing Machinery, New York, NY, USA, 228–231. Link Link
Brudermueller, T., Wirth, F., Weigert, A., & Staake, T. (2022). Automatic differentiation of variable and fixed speed heat pumps with smart meter data. In 2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) (pp. 412-418). IEEE. Link Link
Brudermueller, T., Shung, D. L., Stanley, A. J., Stegmaier, J., & Krishnaswamy, S. (2020). Making logic learnable with neural networks. arXiv preprint arXiv:2002.03847. Link Link
Kreft, M., Brudermueller, T., Fleisch, E., & Staake, T. (2024). Predictability of electric vehicle charging: explaining extensive user behavior-specific heterogeneity. Applied Energy, 370, 123544. Link Link

Pinned Loading

  1. bitstoenergy/iclr-tutorial bitstoenergy/iclr-tutorial Public

    Smart Meter Data Analytics Tutorial @ 11th International Conference on Learning Representations (ICLR 2023)

    Jupyter Notebook 14 5

  2. smd-hp-cycling smd-hp-cycling Public

    Large-scale monitoring of residential heat pump cycling using smart meter data

    Jupyter Notebook 1 2

  3. smd-hp-disaggregation smd-hp-disaggregation Public

    Disaggregation of Heat Pump Load Profiles From Low-Resolution Smart Meter Data

    Jupyter Notebook 3

  4. smd-hp-type-detection smd-hp-type-detection Public

    Automatic Differentiation of Variable and Fixed Speed Heat Pumps With Smart Meter Data

    Python 1

  5. KrishnaswamyLab/logicml KrishnaswamyLab/logicml Public

    C 6