Einzelansicht eines Moduls
Modul (6 Credits)
Deep Learning in Energy
- Name im Diploma Supplement
- 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
Equally weighted average of a group project and a presentation (usually about 20 minutes).
- Verwendung in Studiengängen
- Bestandteile
Vorlesung (3 Credits)
Deep Learning in Energy
- Name im Diploma Supplement
- Deep Learning in Energy
- Anbieter
- Lehrstuhl für Data Science in Energy and Environment
- Lehrperson
- Prof. Dr. Florian Ziel
- Turnus
- unregelmäßig
- SWS
- 2
- Sprache
- englisch
- maximale Hörerschaft
- unbeschränkt
- Hörerschaft
- vgl. Modul
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
- Introduction to electricity markets
- Overview of different non-linear model approaches
- Advanced forcasting study design, (hyperparmeter) optimization/learning, evaluation and ensembling
- 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.
Übung (3 Credits)
Deep Learning in Energy
- Name im Diploma Supplement
- Deep Learning in Energy
- Anbieter
- Lehrstuhl für Data Science in Energy and Environment
- Lehrperson
- Prof. Dr. Florian Ziel
- Turnus
- unregelmäßig
- SWS
- 2
- Sprache
- englisch
- maximale Hörerschaft
- unbeschränkt
- Hörerschaft
- vgl. Modul
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.