Veranstaltungen
Seminar
Data Science in Energy and Environment
- Name im Diploma Supplement
- Advanced Forecasting in Energy Markets
- Anbieter
- Lehrstuhl für Data Science in Energy and Environment
- Lehrperson
- Prof. Dr. Florian Ziel
- Turnus
- unregelmäßig
- SWS
- 2
- Sprache
- englisch
- maximale Hörerschaft
- unbeschränkt
- Hörerschaft
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.