Veranstaltungen

Lecture

Deep Learning in Energy

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)