Einzelansicht eines Moduls
Modul (6 Credits)
Data Science in Energy and Environment
- Name im Diploma Supplement
- Data Science in Energy and Environment
- 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).
In Bezug auf das Niveau der zu erbringenden Leistung erfolgt eine Binnendifferenzierung nach Bachelor- bzw. Masterstudiengang.
Die Prüfung in diesem Modul darf nicht abgelegt werden, wenn "Advanced Forecasting in Energy Markets" bereits bestanden ist.
- Verwendung in Studiengängen
- Bestandteile
Seminar (6 Credits)
Data Science in Energy and Environment
- 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
- Introduction to selected statistical/machine learning/forecasting concepts and techniques
- Evaluation frameworks
- Applications to problems in energy (markets) or the environment.
- 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.
- 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.
- 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