Informationen zu den Modulen

Modul (6 Credits)

Deep Learning in Energy

Verantwortlich
Prof. Dr. Florian Ziel
Voraus­setzungen
Siehe Prüfungsordnung.
Workload
180 Stunden studentischer Workload gesamt, davon:
  • Präsenzzeit: 60 Stunden
  • Vorbereitung, Nachbereitung: 80 Stunden
  • Prüfungsvorbereitung: 40 Stunden
Dauer
Das Modul erstreckt sich über 1 Semester.
Qualifikations­ziele

The students

  • have an advanced understanding of electricity markets and systems
  • understand deep learning based modeling methods for energy markets and systems
  • can apply learning and forecasting algorithms to real data using deep learning software
  • able to interpret and to visualize the results
Prüfungs­modalitäten

The module examination is module-related and takes the form of a written exam (usually 60-90 minutes), or an oral exam (usually 20-30 minute), or a group project (50% of the grade) and presentation (usually 20 minutes, 50% of the grade). The type of examination is determined by the lecturer at the beginning of each semester.

Verwendung in Studiengängen
  • BWL EaF MasterWahlpflichtbereich 1.-3. FS, Wahlpflicht
  • ECMX MasterWahlpflichtbereichME6 Applied Econometrics 1.-3. FS, Wahlpflicht
  • GOEMIK MasterWahlpflichtbereich Bereich Betriebswirtschaftslehre 1.-3. FS, Wahlpflicht
  • MuU MasterWahlpflichtbereich IWahlpflichtbereich I A.: Methodologie und allgemeine Theorien zur Untersuchung von Märkten und Unternehmen 1.-3. FS, Wahlpflicht
  • VWL MasterWahlpflichtbereich II 1.-3. FS, Wahlpflicht
Infos für das aktuelle Semester
    Bestandteile

    Vorlesung (3 Credits)

    Deep Learning in Energy

    Anbieter
    Lehrstuhl für Data Science in Energy and Environment
    Lehrperson
    Prof. Dr. Florian Ziel
    Turnus
    Wintersemester
    SWS
    2
    Sprache
    englisch
    Hörerschaft

    empfohlenes Vorwissen

    Good knowledge of linear models as tought in Econometrics of Electricity Markets and R or python knowledge

    Abstract

    The objective of the lecture is to provide a basic understanding of energy markets and systems such as deep learning based modeling methods with a focus on feed forward neural network and recurrent neural networks. The aim of this course is to understand and apply deep learning algorithms to real data using the pytorch library, to interpret and to visualize the results.

    Lehrinhalte

    1. Introduction to electricity markets
    2. Overview of different non-linear model approaches
    3. Advanced forcasting study design, (hyperparmeter) optimization/learning, evaluation and ensembling
    4. Feed forward and recurrent neural networks and in detail

    Literaturangaben

    The relevant material will be given during the course.

    Suggested reading:

    • Weron, Rafał. "Electricity price forecasting: A review of the state-of-the-art with a look into the future." International Journal of Forecasting 30.4 (2014): 1030-1081.
    • Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Taieb, S. B., ... & Ziel, F. (2022). Forecasting: theory and practice. International Journal of Forecasting, 38(3), 705-871.
    • Marcjasz, G., Narajewski, M., Weron, R., & Ziel, F. (2023). Distributional neural networks for electricity price forecasting. Energy Economics, 125, 106843.
    • Goodfellow, I. (2016). Deep learning.

    didaktisches Konzept

    Lecture. The studied modeling an forecasting methods are applied on real data using pytorch.

    Vorlesung: Deep Learning in Energy (WIWI‑C1265)

    Übung (3 Credits)

    Deep Learning in Energy

    Anbieter
    Lehrstuhl für Data Science in Energy and Environment
    Lehrperson
    Prof. Dr. Florian Ziel
    Turnus
    Wintersemester
    SWS
    2
    Sprache
    englisch
    Hörerschaft

    empfohlenes Vorwissen

    See Lecture

    Lehrinhalte

    See Lecture

    Literaturangaben

    See Lecture

    didaktisches Konzept

    Tutorials. The students apply the learned methods in a own real data project in python utilizing pytorch.

    Übung: Deep Learning in Energy (WIWI‑C1266)
    Modul: Deep Learning in Energy (WIWI‑M0967)