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IEET > Staff > Research Assistants > Béla Wiegel, M.Sc.

Béla Wiegel

M.Sc.
Research Assistant

Contact

Béla Wiegel, M. Sc.
E-6 Elektrische Energietechnik
  • Elektrische Energietechnik
Harburger Schloßstraße 36,
21079 Hamburg
Building HS36, Room C2 1.001
Phone: +49 40 42878 2240
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Research Projects

EffiziEntEE
Efficient integration of high shares of renewable energies in technically and economically integrated energy systems

EffiziEntEE

Efficient integration of high shares of renewable energies in technically and economically integrated energy systems

Federal Ministry for Economic Affairs and Climate Action (BMWK); Duration: 2022 to 2025

to the project page
CyEntEE
I³-Lab Cyber Physical Energy Systems – Sustainability, Resilience and Economics

I³-Lab

CyEntEE

Cyber Physical Energy Systems – Sustainability, Resilience and Economics

Hamburg University of Technology (TUHH); Duration: 2020 to 2023

to the project page

Publications

TUHH Open Research (TORE)

2024

2023

2022

2021

Courses

Stud.IP
link to course in Stud.IP Studip_icon
Machine Learning in Electrical Engineering and Information Technology
Semester:
SoSe 24
Course type:
Lecture
Course number:
lv3004_s24
Lecturer:
Prof. Dr. sc. techn. Christian Schuster, Prof. Dr.-Ing. Christian Becker, Prof. Dr. Alexander Kölpin, Gerhard Bauch, Dr. Maximilian Stark, Dr. Davood Babazadeh, Dr. Cheng Yang, PD Dr.-Ing. habil. Rainer Grünheid, Simon Stock, M.Sc.
Description:
This master course, a collaborative effort between the Institute of Communications, the Institute for High-Frequency Engineering, the Institute for Power Systems, and the Institute for Theoretical Electrical Engineering, is designed to unveil the synergies between machine learning and our respective fields of expertise. In an age defined by rapid technological advancement, machine learning stands as a catalyst for innovation, offering transformative possibilities across diverse sectors. From optimizing communication systems to enhancing power grid efficiency, and from refining signal processing techniques to enabling autonomous systems, the integration of machine learning techniques holds immense promise for addressing contemporary challenges. Throughout this course, we will delve into the theoretical underpinnings, practical methodologies, and tangible applications of neural networks and machine learning algorithms. By delving into algorithmic design, data analysis, and optimization techniques, we aim to equip you with the skills and insights needed to navigate the complexities of modern engineering landscapes.
Performance accreditation:
m1785-2022 - Machine Learning in Electrical Engineering and Information Technology<ul><li>p1778-2022 - Machine Learning in Electrical Engineering and Information Technology: mündlich</li></ul>
ECTS credit points:
6
Stud.IP informationen about this course:
Home institute: Institut für Nachrichtentechnik (E-8)
Registered participants in Stud.IP: 102
Postings: 2
Documents: 1

Supervised Theses

ongoing
completed

2022

  • Kaya, E. (2022). Simulation des Lebenszyklus‘ einer Lithium Ion Zelle in den stationären EP and instationären EV Anwendungsfällen.

  • Pauelsen, F.-T. (2022). Implementierung eines Maximum-Power-Point-Tracker für Photovoltaikanlagen in Modelica.

  • Rücker, J. (2022). Dynamische Untersuchung des Verhaltens elektrischer Komponenten auf Quartiersebene hinsichtlich der Spannungshaltung.

  • Rüffert, J. (2022). Charakterisierung von Zellen in Verteilnetzen anhand von Bewertungskriterien und die Auswirkungen von punktuell und zeitlich begrenzt auftretenden Lasten.

2021

  • Helmrich von Elgott, L. (2021). Optimierter Einsatz dezentraler Flexibilität zur Betriebsführung intelligenter sektorgekoppelter Verteilnetze.

  • Zwinzscher, S. (2021). Entwicklung einer Methodik zur dynamischen Berechnung der Flexibilität eines auf Power-to-Heat basierenden Nahwärmenetzes.

TU Hamburg
Institute of Electrical Power and Energy Technology (ieet) E-6
Prof. Dr.-Ing. Christian Becker
Harburger Schloßstraße 36
21079 Hamburg
E-mail : ieet(at)tuhh.de
Phone : +49 40 42878 3213
Fax : +49 40 42878 2382
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