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Module (6 Credits)

Deep Learning in Energy

Name in diploma supplement
Deep Learning in Energy
Responsible
Prof. Dr. Florian Ziel
Admission criteria
See exam regulations.
Workload
180 hours of student workload, in detail:
  • Attendance: 60 hours
  • Preparation, follow up: 80 hours
  • Exam preparation: 40 hours
Duration
The module takes 1 semester(s).
Qualification Targets

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
Module Exam

Equally weighted average of a group project and a presentation (usually about 20 minutes).

Usage in different degree programs
  • BWL EaF MasterWahlpflichtbereich 1.-3. Sem, Elective
  • ECMX MasterWahlpflichtbereichME6 Applied Econometrics 1.-3. Sem, Elective
  • MuU MasterWahlpflichtbereich IWahlpflichtbereich I A.: Methodologie und allgemeine Theorien zur Untersuchung von Märkten und Unternehmen 1.-3. Sem, Elective
  • VWL MasterWahlpflichtbereich II 1.-3. Sem, Elective
Elements
Name in diploma supplement
Deep Learning in Energy
Organisational Unit
Lehrstuhl für Data Science in Energy and Environment
Lecturers
Prof. Dr. Florian Ziel
SPW
2
Language
English
Cycle
irregular
Participants at most
no limit
Participants

Preliminary knowledge

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.

Contents

  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

Literature

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.

Teaching concept

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

Lecture: Deep Learning in Energy (WIWI‑C1265)
Name in diploma supplement
Deep Learning in Energy
Organisational Unit
Lehrstuhl für Data Science in Energy and Environment
Lecturers
Prof. Dr. Florian Ziel
SPW
2
Language
English
Cycle
irregular
Participants at most
no limit
Participants

Preliminary knowledge

See Lecture

Contents

See Lecture

Literature

See Lecture

Teaching concept

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

Exercise: Deep Learning in Energy (WIWI‑C1266)
Module: Deep Learning in Energy (WIWI‑M0967)