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IEET > Staff > Research Assistants > Johannes Heise, M.Sc.

Johannes Heise

M.Sc.
Research Assistant

Contact

Johannes Heise, M.Sc.
E-6 Elektrische Energietechnik
  • Elektrische Energietechnik
Harburger Schloßstraße 36,
21079 Hamburg
Building HS36, Room C3 0.009
Phone: +49 40 42878 4099
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Research Project

iNeP
Integrated network planning for the electricity, gas and heat sectors

iNeP

Integrated network planning for the electricity, gas and heat sectors

Federal Ministry for Economic Affairs and Climate Action (BMWK); Duration: 2021 to 2026

to the project page

Publications

TUHH Open Research (TORE)

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: 101
Postings: 2
Documents: 1

Supervised Theses

ongoing

2024

  • Westphal, M (2024). Optimierte Planung eines gekoppelten Verteilnetzes unter der Berücksichtigung flexibler Komponenten.

2023

  • Hülfenhaus, V (2023). Modellierung und Planung eines urbanen sektorgekoppelten Verteilnetzes.

completed

2023

  • Körber, C (2023). Quantifizierung und Modellierung von Flexibilitätsoptionen im Mittelspannungsverteilnetz für eine optimierte Netzplanung.

  • Oboreh, J (2023). Einsatz von Gas-gefeuerten Blockheizkraftwerken zur Stabilisierung des Stromnetzes bei hoher Durchdringung von Wärmepumpen.

  • Velikov, S (2023). Entwicklung und Parametrisierung eines Wärmepumpen- und Speichermodells für die Netzberechnung und -planung.

  • Westphal, M (2023). Aggregation von Flexibilitäten im Niederspannungsnetz zur Netzplanung unter Berücksichtigung der Auslastung von Betriebsmitteln.

2022

  • Albrecht, J. P. (2022). Entwicklung einer netzdienlichen Regelungsstrategie für einen Elektrolyseur im Verteilnetz.

  • Barthelme, J. (2022). Technisch-ökonomische Systemmodellierung und -anlayse eines urbanen Quatiers hinsichtlich des Einsatz von Wasserstoff als primärer Energieträger.

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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