Informations about the modules


Module (6 Credits)

Advanced Forecasting in Energy Markets

Name in diploma supplement
Advanced Forecasting in Energy Markets
Admission criteria
See exam regulations.
180 hours of student workload, in detail:
  • Attendance: 30 hours
  • Preparation, follow up: 110 hours
  • Exam preparation: 40 hours
The module takes 1 semester(s).
Qualification Targets

The students

  • have an advanced understanding of forecasting concepts and techniques applied in energy markets
  • will use statistical software R to fit estimation and forecasting algorithms to real world data
  • can visualize and interpret obtained results
Module Exam

Weighted average of a group R-project and a presentation (usually about 20 minutes).

Usage in different degree programs
  • BWL EaFSeminarbereich2nd-3rd Sem, Elective
  • ECMXWahlpflichtbereichME5 Economics1st-3rd Sem, Elective
  • MuUSeminarbereich Märkte und Unternehmen2nd-3rd Sem, Elective
  • VWLSeminarbereich2nd-3rd Sem, Elective
Name in diploma supplement
Advanced Forecasting in Energy Markets
Organisational Unit
Participants at most
Preliminary knowledge

Good knowledge of linear models and autoregressive processes. Experienced R knowledge. Sucessful participation in Econometrics of Electricity Markets is very helpful.


The purpose of this seminar is to provide an advanced understanding of modeling and forecasting methods in energy markets, esp. concerning probabilistic forecasting. The students apply sophisticated forecasting methods to real data (e.g. electricity or natural gas prices, electricity load, wind and solar power production) using the statistical Software R. They write a report and present their findings.

The focus of the seminar is placed especially on probabilistic forecasting with different applications in e.g. electricity price and electricity load or wind and solar power production forecasting. A particular attention is given to regression-based modeling methods for electricity market data.

  1. Introduction to probabilistic forecasting
  2. Forecasting evaluation in probabilistic forecasting frameworks
  3. Applications to energy market data
  • Hong, T., Pinson, P., Fan, S., Zareipour, H., Troccoli, A., & Hyndman, R. J. (2016). Probabilistic energy forecasting: Global energy forecasting competition 2014 and beyond.
  • Nowotarski, J., & Weron, R. (2017). Recent advances in electricity price forecasting: A review of probabilistic forecasting. Renewable and Sustainable Energy Reviews.
Teaching concept

In the first few weeks the students learn the concepts of probabilistic forecasting in classes. Afterwards they apply the methods to energy market data using R, write a report and present their results.

Seminar: Advanced Forecasting in Energy Markets (WIWI‑C1106)
Module: Advanced Forecasting in Energy Markets (WIWI‑M0796)