Econometrics of Electricity Markets

Module: Econometrics of Electricity Markets (6 Credits)
Name in diploma supplement	Econometrics of Electricity Markets
Responsible	Prof. Dr. Florian Ziel
Admission criteria	See exam regulations.
Workload	180 hours of student workload, in detail: Attendance: 60 hours Preparation, follow up: 80 hours Exam preparation: 40 hours
Duration	The module takes 1 semester(s).
Qualification Targets	The students have an advanced understanding of electricity markets understand regression based modeling methods for electricity prices can apply estimation and forecasting algorithms to real data using the statistical Software R able to interpret and to visualize the results
Module Exam	Equally weighted average of a group R-project and a presentation (usually about 20 minutes).
Usage in different degree programs	BWL EaF Master > Wahlpflichtbereich > 1.-3. Sem, Elective ECMX Master > Wahlpflichtbereich > ME6 Applied Econometrics > 1.-3. Sem, Elective MuU Master > Wahlpflichtbereich I > Wahlpflichtbereich I A.: Methodologie und allgemeine Theorien zur Untersuchung von Märkten und Unternehmen > 1.-2. Sem, Elective VWL Master > Wahlpflichtbereich I > 1.-3. Sem, Elective
Elements	Lecture Econometrics of Electricity Markets (3 Credits) Exercise Econometrics of Electricity Markets (3 Credits)
Module: Econometrics of Electricity Markets (WIWI‑M0788)

Lecture: Econometrics of Electricity Markets (3 Credits)
Name in diploma supplement	Econometrics of Electricity Markets
Organisational Unit	Lehrstuhl für Data Science in Energy and Environment
Lecturers	Prof. Dr. Florian Ziel
Hours per week	2	Language	English
Cycle	irregular	Participants at most	24
Preliminary knowledge Good knowledge of linear models. R knowledge (esp. functions like lm) Understanding of AR(p) processes is very helpful
Abstract The objective of the lecture is to provide a basic understanding of electricity markets and regression based modeling methods for electricity prices. The aim of this course is to apply estimation and forecasting algorithms to real data using the statistical Software R, to interpret and to visualize the results.
Contents Introduction to electricity markets Overview of different model approaches Regression based modeling methods for electricity prices Forcasting and evaluation techniques Advanced estimation and modeling approaches
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.
Teaching concept Lecture. The studied modeling an forecasting methods are applied on real data using the statistical sofware R.
Lecture: Econometrics of Electricity Markets (WIWI‑C1073)

Exercise: Econometrics of Electricity Markets (3 Credits)
Name in diploma supplement	Econometrics of Electricity Markets
Organisational Unit	Lehrstuhl für Data Science in Energy and Environment
Lecturers	Prof. Dr. Florian Ziel
Hours per week	2	Language	English
Cycle	irregular	Participants at most	24
Preliminary knowledge See Lecture
Contents See Lecture
Literature See Lecture
Teaching concept Tutorials. The students apply the learned methods in a own real data project.
Exercise: Econometrics of Electricity Markets (WIWI‑C1126)

Module: Econometrics of Electricity Markets (6 Credits)