MASTER THESIS – COMPUTER VISION FOR RENEWABLE ENERGY SYSTEMS (M/F/D)
fortiss is the research institute of the Free State of Bavaria for software-intensive systems and services with headquarters in Munich. The institute currently employs around 180 employees, who collaborate on research, development and transfer projects with universities and technology companies in Bavaria, Germany and Europe. Research is focused on state-of-the-art methods, techniques and tools of software development, systems & service engineering and their application to reliable, secure cyber-physical systems, such as the Internet of Things (IoT). fortiss has the legal structure of a non-profit limited liability company (GmbH). Its shareholders are the Free State of Bavaria (as majority shareholder) and the Fraunhofer Society for the Promotion of Applied Research. www.fortiss.org
Master Thesis - Computer vision for renewable energy systems (m/f/d)
Due to their relatively small cost, PV systems offer the participation in the “Energiewende” for millions of house owners in Germany. Size and Location of this large number of installed PV systems are included in the Marktstammdatenregister from the Bundesnetzagentur, but accessing this data is restricted. However, knowing the current number, capacity and location of installations is not only important for energy system modelling, but also for policy makers.
This thesis aims at extracting relevant information about PV systems from a data set of flight images by using computer vision techniques. The data set contains up-to-date images of Munich from recent overflights. Besides creating and optimizing Neural Networks dedicated for the task of object identification, the student will also validate the synthetic data by comparing it with the Marktstammdatenregister. The work will be accompanied by fortiss and the industrial partner eniano GmbH.
Goals of thesis:
- Building a Neural Network for the task of Semantic Segmentation
- Extracting Sizes and Locations of PV installations in Munich
- Validate the synthetic data
- Validating the synthetic data by comparing it to other sources of data
- Studies of informatics, electrical engineering or similar
- Interest in energy-related topics
- Knowledge of python
- Basic knowledge of machine learning and neural networks
- Good communication skills in English, basic skills in German are a plus
- Varied and future-oriented field of activity
- Young, dynamic and team-oriented working atmosphere
- Space for new ideas, for assuming responsibility, as well as for personal and professional development
- Cooperation with industry and the research facilities of the Technical University of Munich
- Please note that this Master thesis is not remunerated
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Contact: Dr. Markus Duchon