Teaching Activities and Open Thesis Topics

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This blog post serves to summarise courses and other teaching activities with focus on Smart Grids and Energy Informatics. These courses are offered by the Smart Grids Group (University of Klagenfurt) and contribute to the Bachelor’s programme Informationstechnik as well as the Master’s programme on Information and Communications Engineering (ICE).

As the research field of Smart Grids is an interdisciplinary one, we also warmly welcome students from other domains such as computer science or mathematics!

ProgrammeIDTypeName
Bachelor700.006CourseGrundlagenlabor: Energieinformatik
Bachelor700.286SeminarSeminar zu Smart Grids
Master700.267SeminarResearch Seminar on Smart Grids
Master700.285LectureSmart Grids
Master700.284CourseSmart Microgrid Lab
Any700.288InternshipResearch Project in Smart Grids

Projects and Theses

Beside weekly courses, the Smart Grids research group invites students to conduct their Bachelor/Master thesis. Furthermore, it’s possible to join our research group for a while by means of a Research Project in Smart Grids. Though open topics are announced to students via newsletter, the actual content and goals of a thesis are discussed individually and can be set in a way to meet the special interests of students.

A selection of recently-accepted thesis topics:

  • Design and Verification of a low-cost Energy Monitor
  • Design of an Artificial Energy Consumption Tool
  • Analysing Smart Meter Data for Detection of Usage Patterns and Abnormal Behavior
  • Novel Deep Learning Techniques for Load Disaggregation

OPEN: Forecast of energy consumption in the residential sector

The energy consumption of users can be seen as an aspect of human behaviour. Without a doubt, this behaviour is influenced by weather conditions. When trained with smart meter readings, neural networks can be applied to predict the energy consumption of households. The question is, if weather forecast can serve as adequate training data to successfully predict the energy consumption of households. The scope of this thesis will be to explore this question.

Type: Scalable to all thesis types

OPEN: Supervised learning techniques for energy advisors

Energy Advisors such as Mjölnir provide valuable feedback to the user. The feedback builds on gathered knowledge and observations of the energy consumption in households. The objective of this thesis will be:

  • Review applicable supervised machine learning techniques and discuss their application in Energy Advisor tools
  • Implement the most preferable technique and embed it into the Mjölnir framework
  • Evaluate the performance by means of a case study

Type: Scalable to all thesis types

Interested?

Prospective students are warmly welcome to drop me an email at any time!

Contact: christoph.klemenjak@aau.at

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