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Modul (6 Credits)

Advanced Forecasting in Energy Markets

Name im Diploma Supplement
Advanced Forecasting in Energy Markets
Verantwortlich
Voraus­setzungen
Siehe Prüfungsordnung.
Workload
180 Stunden studentischer Workload gesamt, davon:
  • Präsenzzeit: 30 Stunden
  • Vorbereitung, Nachbereitung: 110 Stunden
  • Prüfungsvorbereitung: 40 Stunden
Dauer
Das Modul erstreckt sich über 1 Semester.
Qualifikations­ziele

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
Prüfungs­modalitäten

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

Verwendung in Studiengängen
  • BWL-EaF-Ma-2015Seminarbereich2.-3. FS, Pflicht
  • ECMX-Ma-2019WahlpflichtbereichBereich Economics1.-3. FS, Pflicht
  • MuU-Ma-2013Seminarbereich Märkte und Unternehmen2.-3. FS, Pflicht
  • VWL-Ma-2009-V2013Seminarbereich2.-3. FS, Pflicht
Bestandteile
Name im Diploma Supplement
Advanced Forecasting in Energy Markets
Anbieter
Lehrperson
SWS
2
Sprache
englisch
Turnus
unregelmäßig
maximale Hörerschaft
20
empfohlenes Vorwissen

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

Abstract

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.

Lehrinhalte
  1. Introduction to probabilistic forecasting
  2. Forecasting evaluation in probabilistic forecasting frameworks
  3. Applications to energy market data
Literaturangaben
  • 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.
didaktisches Konzept

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.

Hörerschaft
  • BWL-EaF-Ma-2015SeminarbereichModul "Advanced Forecasting in Energy Markets"2.-3. FS, Pflicht
  • ECMX-Ma-2019WahlpflichtbereichBereich EconomicsModul "Advanced Forecasting in Energy Markets"1.-3. FS, Pflicht
  • MuU-Ma-2013Seminarbereich Märkte und UnternehmenModul "Advanced Forecasting in Energy Markets"2.-3. FS, Pflicht
  • VWL-Ma-2009-V2013SeminarbereichModul "Advanced Forecasting in Energy Markets"2.-3. FS, Pflicht
Seminar: Advanced Forecasting in Energy Markets (WIWI‑C1106)
Modul: Advanced Forecasting in Energy Markets (WIWI‑M0796)