IPE 16-2022 Bachelor / Master Thesis
Smart energy: Federated Learning for secure load disaggregation

Organisationseinheit

Institut für Prozessdatenverarbeitung und Elektronik (IPE)

Ihre Aufgaben

The energy transition and the resulting expansion of renewable energy resources increasingly pose a challenge to the energy system due to their volatile and intermittent nature. In this context, smart meters are central as they monitor and forecast energy flows. While installing multiple smart meters requires high investments, load disaggregation can identify individual devices within the aggregated power data based on unique load characteristics. However, using smart meter data for load disaggregation is challenging due to data privacy requirements. One solution to improve data privacy is federated learning, as data is kept private, and only the trained machine learning models are merged and updated on a global server. In your thesis, you analyze how federated and transfer learning can improve data privacy and accuracy for load disaggregation.

Your Thesis:

• Review: You will review the state-of-the-art research on artificial neural networks, including deep neural networks, recurrent neural networks, and transformers. Further, you consider publications in the areas of load disaggregation and federated learning.
• Architecture concept: Based on your research, you propose a federated learning architecture for load disaggregation.
• Implement: You will implement your concept with a selected algorithm on real datasets.
• Evaluate: You will thoroughly evaluate the experiments.

Eintrittstermin

Immediately

Ihre Qualifikation

• You study Computer Science, engineering, industrial engineering, or a related course of study
• You are deeply interested in topics such as artificial intelligence, deep learning, energy systems, or load disaggregation
• You are able to read and write scientific texts in English or German
• You already have some experience in Python
• You show an above-average degree of initiative and commitment, as well as a thorough way of working