Data Analytics and Intelligent Systems in Energy Informatics (IN0014, IN2107, IN4725)

Lecturer (assistant)
TypeAdvanced seminar
Duration2 SWS
TermSommersemester 2021
Language of instructionEnglish
Position within curriculaSee TUMonline
DatesSee TUMonline


  • 12.04.2021 14:00-16:00 Online: Videokonferenz / Zoom etc.
  • 19.04.2021 14:00-16:00 Online: Videokonferenz / Zoom etc.
  • 26.04.2021 14:00-16:00 Online: Videokonferenz / Zoom etc.
  • 03.05.2021 14:00-16:00 Online: Videokonferenz / Zoom etc.
  • 10.05.2021 14:00-16:00 Online: Videokonferenz / Zoom etc.
  • 17.05.2021 14:00-16:00 Online: Videokonferenz / Zoom etc.
  • 31.05.2021 14:00-16:00 Online: Videokonferenz / Zoom etc.
  • 07.06.2021 14:00-16:00 Online: Videokonferenz / Zoom etc.
  • 14.06.2021 14:00-16:00 Online: Videokonferenz / Zoom etc.
  • 21.06.2021 14:00-16:00 Online: Videokonferenz / Zoom etc.
  • 28.06.2021 14:00-16:00 Online: Videokonferenz / Zoom etc.
  • 05.07.2021 14:00-16:00 Online: Videokonferenz / Zoom etc.
  • 12.07.2021 14:00-16:00 Online: Videokonferenz / Zoom etc.

Admission information

See TUMonline
Note: The pre-liminary meeting will take place on Monday 1st of February 2:00 p.m. via Please login and use your tum credentials access code: 363503


Deep dive into a practical machine learning topic. Learn how future energy solutions might look like. Learn how to answer a research question. Learn how to read and judge scientific papers. Learn how to build prototypes of algorithms. Learn how to present scientific work. Learn scientific writing skills.


Today's electric power grids and supplies are cyber-physical systems, where information and communication technology (ICT) play an important role in reliably operating all system components. Electrical data can be recorded everywhere, for example in households, office buildings and industrial plants. Analyzing such data enables a wide variety of possible applications. Furthermore, machine learning on electrical time-series data poses multiple challenges to both algorithms, platform design and data privacy. In this seminar, students will be able to make own research contributions to this research area. They can dive into the different areas of energy informatics, with focus on the various machine learning techniques applied to this research field and the applications for Smart Homes or Smart Factories which are based on the results of such analysis. Applications based on electrical data analysis range from detecting individual appliances in households to condition monitoring and predictive maintenance on industrial plants. The wide distribution of sensors in the context of “Industry 4.0 scenarios” further enables such applications and it is of particular interest to investigate the use of electrical data to facilitate the development of smart factories. The topics in this seminar are suited for both students with beginner or advanced knowledge in machine learning and ones interested in it from a higher-level application and architectural perspective. The topics consist of conducting literature research and some of them also require the development of a prototype.

Teaching and learning methods

Conference like structur with writing your own report, reviewing one from another group and presenting your findings with a presentation.