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Note: The pre-liminary meeting will take place on Wednesday 25th of January 2023 at 02:00 p.m. via https://bbb.rbg.tum.de/ren-zh3-yr3

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. Deep dive into a practical machine learning topic. Learn how future energy solutions might look like

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. Different stakeholder measure electrical and thermal data. This can include heat pumps in private households, electricity consumption in office buildings or a combination of both in industrial plants. Analyzing such data enables a wide variety of possible applications. Furthermore, machine learning on energy 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 will dive into 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 consist of conducting literature research and/or coding your own prototype. The pre-liminary meeting will take place on Wednesday 25th of January 2023 at 02:00 p.m. via https://bbb.rbg.tum.de/ren-zh3-yr3

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.

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