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Keine Credits bei Lehrveranstaltungen angegeben

Bei den Modulen unten sind Credits angegeben, bei der (modulunabhängigen) Lehrveranstaltungsliste nicht. Dies liegt darin begründet, dass die Lehrveranstaltungen erst im Kontext eines Modules Credits erhalten. Auch wenn der Fall selten eintritt, ist so die Möglichkeit gegeben, dass die selbe Veranstaltung in unterschiedlichen Studiengängen unterschiedlichen Workload und Credits erhalten kann.

Üblicherweise gilt aber weiterhin natürlich die Faustregel Cr = 1,5 * SWS. 

create MS Word export

If you like to create a change request for the modules, the easiest way is to export this list and then use the "track changes" functionality in MS Word and send the new file to AG Modulhandbuch. As a starting point you can use the word-export above.


https://www.dsee.wiwi.uni-due.de/

Lehrstuhl für Data Science in Energy and Environment

assigned LecturersKulakov (M.Sc. Sergei Kulakov)
wissenschaftliche Mitarbeiter(innen) ( wissenschaftliche Mitarbeiter(innen))
Ziel (Prof. Dr. Florian Ziel)

Responsbile for the modules

Name in diploma supplement
Advanced Forecasting in Energy Markets
Responsible
Admission criteria
See exam regulations.
Workload
180 hours of student workload, in detail:
  • Attendance: 30 hours
  • Preparation, follow up: 110 hours
  • Exam preparation: 40 hours
Duration
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 EaFSeminarbereich2.-3. Sem, Elective
  • ECMXWahlpflichtbereichME5 Economics1.-3. Sem, Elective
  • MuUSeminarbereich Märkte und Unternehmen2.-3. Sem, Elective
  • VWLSeminarbereich2.-3. Sem, Elective
Elements
  • SEM: Advanced Forecasting in Energy Markets (6 Credits)
Module: Advanced Forecasting in Energy Markets (WIWI‑M0796)

Name in diploma supplement
Deep Learning in Energy
Responsible
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
Module Exam
Usage in different degree programs
    Module: Deep Learning in Energy (WIWI‑M0967)

    Name in diploma supplement
    Econometrics of Electricity Markets
    Responsible
    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 EaFWahlpflichtbereich1.-3. Sem, Elective
    • ECMXWahlpflichtbereichME6 Applied Econometrics1.-3. Sem, Elective
    • MuUWahlpflichtbereich IWahlpflichtbereich I A.: Methodologie und allgemeine Theorien zur Untersuchung von Märkten und Unternehmen1.-2. Sem, Elective
    • VWLWahlpflichtbereich I1.-3. Sem, Elective
    Elements
    • VO: Econometrics of Electricity Markets (3 Credits)
    • UEB: Econometrics of Electricity Markets (3 Credits)
    Module: Econometrics of Electricity Markets (WIWI‑M0788)

    Name in diploma supplement
    Energy Forecasting Competition
    Responsible
    Admission criteria
    See exam regulations.
    Workload
    180 hours of student workload, in detail:
    • Attendance: 60 hours
    • Preparation, follow up: 100 hours
    • Exam preparation: 20 hours
    Duration
    The module takes 1 semester(s).
    Qualification Targets

    The students

    • learn concepts to produce and evaluate probabilistic forecasts
    • can produce forecasts using python or R for time series data from energy systems and markets
    • learn basics about forecasting competitons
    • learn characteristics of energy time series data sets (e.g. including energy consumption, energy prices, wind and solar production, etc.)
    • learn to visualize, report and present results
    Relevance

    The module is highly relevant for practice, not only in the energy industy. Students acquire skills that are useful in data projects, operations and evaluation.

    Module Exam

    Zum Modul erfolgt eine modulbezogene Prüfung in Form der Entwicklung eines Prognosemodells (20 % der Note), Ausarbeitung zum Modell (Hausarbeit, 50% der Note) sowie Präsentation (in der Regel: 20-40 Minuten, 30 % der Note).

    Usage in different degree programs
    • BWL EaFWahlpflichtbereich1.-3. Sem, Elective
    • ECMXWahlpflichtbereichME5 Economics1.-3. Sem, Elective
    • GOEMIKWahlpflichtbereich Bereich Betriebswirtschaftslehre1.-3. Sem, Elective
    • MuUWahlpflichtbereich IIWahlpflichtbereich II B.: Märkte und Unternehmen aus Marktperspektive1.-3. Sem, Elective
    • VWLWahlpflichtbereich II1.-3. Sem, Elective
    Elements
    • VIS: Energy Forecasting Competition (6 Credits)
    Module: Energy Forecasting Competition (WIWI‑M0906)

    Name in diploma supplement
    Portfolio Management
    Responsible
    Admission criteria
    See exam regulations.
    Workload
    180 hours of student workload, in detail:
    • Attendance: 60 hours
    • Preparation, follow up: 90 hours
    • Exam preparation: 30 hours
    Duration
    The module takes 1 semester(s).
    Qualification Targets

    Students

    • have an advanced understanding in portfolio management
    • study modern portfolio optimization methods that take uncertainty into account
    • are able to apply the portfolio theory to real problems, especially in financial and commodity markets
    Module Exam

    The module-related examination is performed by a written exam (usually 90-120 minutes).

    Usage in different degree programs
    • BWL EaFWahlpflichtbereich1.-3. Sem, Elective
    • ECMXWahlpflichtbereichME5 Economics1.-3. Sem, Elective
    • GOEMIKWahlpflichtbereich Bereich Betriebswirtschaftslehre1.-3. Sem, Elective
    • MuUWahlpflichtbereich IIIWahlpflichtbereich III A.: Märkte und Unternehmen aus Unternehmensperspektive1.-3. Sem, Elective
    • VWLWahlpflichtbereich II1.-3. Sem, Elective
    Elements
    • VO: Portfolio Management (3 Credits)
    • UEB: Portfolio Management (3 Credits)
    Module: Portfolio Management (WIWI‑M0880)

    Name in diploma supplement
    Environmental Economics and Renewable Energy
    Responsible
    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

    Die Studierenden

    • erörtern die Ursachen für Marktversagen in umweltwirtschaftlichen Kontexten
    • können Themen der Regulierung in der Umweltökonomik erläutern, insbesondere zum Umgang mit externen Effekten
    • wenden grundlegende Begriffe, Konzeptionen, Modelle und Theorien mit Hilfe von mathematischen und statistischen Methoden auf unterschiedliche spezifische Sachverhalte an
    • analysieren und vergleichen hierbei wissenschaftliche Positionen und Modelle im umwelt- und energiepolitischen Umfeld
    • kennen und verstehen die Charakterisika euneuerbarer Energien, insbesondere von Wind- und Solarenergie
    • beurteilen reale politische und ökonomische Sachverhalte und begründen und untermauern diese mit wirtschaftswissenschaftlichen Argumenten , insbesondere im Bereich der erneuerbarer Energien
    Module Exam

    Zum Modul erfolgt eine modulbezogene Prüfung in der Gestalt einer Klausur (in der Regel: 90-120 Minuten).

    Die Prüfung in diesem Modul darf nicht abgelegt werden, wenn "Energie- und Umweltpolitik" bereits bestanden ist.

    Usage in different degree programs
    • BWLVertiefungsstudiumWahlpflichtbereichVertiefungsbereich Betriebswirtschaftslehre4.-6. Sem, Elective
    • EnergyScEnergiewissenschaft IV7.-8. Sem, Elective
    • LA gbF/kbF BKBachelorprüfung in der kleinen beruflichen FachrichtungProduktion, Logistik, AbsatzWahlpflichtbereich Kleine berufliche Fachrichtung "Produktion, Logistik, Absatz"4.-6. Sem, Elective
    • VWLVertiefungsstudiumWahlpflichtbereichBereich BWL, Recht, Wirtschaftsinformatik, InformatikVertiefungsbereich Betriebswirtschaftslehre4.-6. Sem, Elective
    • WiIngWahlpflichtbereich Wirtschaftswissenschaften, Energiewirtschaft1.-5. Sem, Elective
    • WiMatheVWL-Energie1.-6. Sem, Elective
    • WiMatheVWL-M I1.-6. Sem, Elective
    Elements
    • VO: Umweltökonomik und erneuerbare Energien (3 Credits)
    • UEB: Umweltökonomik und erneuerbare Energien (3 Credits)
    Module: Umweltökonomik und erneuerbare Energien (WIWI‑M0797)

    Offered Courses

    Name in diploma supplement
    Advanced Forecasting in Energy Markets
    Organisational Unit
    Lecturers
    SPW
    2
    Language
    English
    Cycle
    irregular
    Participants at most
    20
    Preliminary knowledge

    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.

    Contents
    1. Introduction to probabilistic forecasting
    2. Forecasting evaluation in probabilistic forecasting frameworks
    3. Applications to energy market data
    Literature
    • 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.

    Participants
    Seminar: Advanced Forecasting in Energy Markets (WIWI‑C1106)
    Name in diploma supplement
    Econometrics of Electricity Markets
    Organisational Unit
    Lecturers
    SPW
    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
    1. Introduction to electricity markets
    2. Overview of different model approaches
    3. Regression based modeling methods for electricity prices
    4. Forcasting and evaluation techniques
    5. 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.

    Participants
    Lecture: Econometrics of Electricity Markets (WIWI‑C1073)
    Name in diploma supplement
    Econometrics of Electricity Markets
    Organisational Unit
    Lecturers
    SPW
    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.

    Participants
    Exercise: Econometrics of Electricity Markets (WIWI‑C1126)
    Name in diploma supplement
    Energy Forecasting Competition
    Organisational Unit
    Lecturers
    SPW
    4
    Language
    English
    Cycle
    irregular
    Participants at most
    no limit
    Preliminary knowledge

    Basics in R or python, basics in data science or statistics.

    Abstract

    In the first third of the Module the students study the competition design, the forecast evaluation methods, benchmark methods and forecasting principles in general in a lecture. The competition task and the corresponding data sets will be released immediately. In the second part the student construct their own forecasting model for the competition and submit their forecasts. Shortly afterwards the results will be released. In the third part of the students write a report on the prediction methods and present their finding.

    Contents
    1. Introduction on forecasting competitions
    2. Competition design and reporting of forecasts
    3. Evaluation metrics
    4. Benchmark methods
    5. Options for improving forecasts
    Literature
    • Hong, T., Pinson, P., Fan, S., Zareipour, H., Troccoli, A., & Hyndman, R. J. (2016). Probabilistic energy forecasting: Global energy forecasting competition 2014 and beyond.  International Journal of Forecasting, 32(3), 896-913.
    • Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2020). The M4 Competition: 100,000 time series and 61 forecasting methods. International Journal of Forecasting, 36(1), 54-74.
    • Further Literature will be mentioned during the lecture.
    Teaching concept

    Classic lectures + Learning by doing

    Die Veranstaltung entspricht einem Vorlesungsanteil von 2 SWS und einem Seminaranteil von 2 SWS.

    Participants
    Lecture with integrated Seminar: Energy Forecasting Competition (WIWI‑C1160)
    Name in diploma supplement
    Portfolio Management
    Organisational Unit
    Lecturers
    SPW
    2
    Language
    English
    Cycle
    irregular
    Participants at most
    no limit
    Preliminary knowledge

    matrix algebra and multivariate statistics (esp. multivariate normal distribution)

    Abstract

    The students study the general Markowitz portfolio theory on optimal portfolio selection with and without risk-free asset. They study problems in the application concerning estimation risk, like the Jobson-Korkie experiment and possible solutions. The theory is applied to problem in financial and commodity markets.

    Contents
    • Introduction to portfolio theory
    • Markowitz portfolio theory without risk-free asset
    • Markowitz portfolio theory with risk-free asset
    • Estimation risk and Jobson-Korkie experiment
    • Optimal portfolio allocation under parameter uncertainty
    Literature
    • Brandt, M. W. (2009). Portfolio choice problems. Handbook of financial econometrics, 1, 269-336.
    • Kan, R., & Zhou, G. (2007). Optimal portfolio choice with parameter uncertainty. Journal of Financial and Quantitative Analysis, 42(3), 621-656.
    • Tu, J., & Zhou, G. (2011). Markowitz meets Talmud: A combination of sophisticated and naive diversification strategies. Journal of Financial Economics, 99(1), 204-215.
    Teaching concept

    The students study portfolio management theory in the lecture. They discuss and apply the theory in tutorials.

    Participants
    Lecture: Portfolio Management (WIWI‑C1127)
    Name in diploma supplement
    Portfolio Management
    Organisational Unit
    Lecturers
    SPW
    2
    Language
    English
    Cycle
    irregular
    Participants at most
    no limit
    Preliminary knowledge

    See Lecture

    Contents

    See Lecture

    Literature

    See Lecture

    Teaching concept

    See Lecture

    Participants
    Exercise: Portfolio Management (WIWI‑C1128)
    Name in diploma supplement
    Environmental Economics and Renewable Energy
    Organisational Unit
    Lecturers
    SPW
    2
    Language
    German
    Cycle
    winter semester
    Participants at most
    no limit
    Preliminary knowledge

    Grundkenntnisse in Mikroökonomik, Mathematik und Statistik sind sehr hilfreich. Grundkenntnisse in Energieökonomik sind hilfreich.

    Abstract

    In der Vorlesung werden im ersten Teil Grundkenntnisse und der Umweltökonomik gelehrt. Neben der ensprechenden Theorie, werden insbesondere Klima und Energie bezogene Problemstellungen diskutiert. Im zweiten Teil der Vorlesung werden erneuerbare Energien im wirtschaftswissenschaftlichen Kontext behandelt. Hierbei liegt der Fokus auf der empirischen und theoretischen Analyse von Wind- und Solarenergie.

    Contents
    1. Grundbegriffe der Umweltökonomik (wie Ökosystem, Nachhaltigkeit, Marktversagen, Öffentliche Güter, Externalitäten)
    2. Internalisierung Externer Effekte
    3. Grundkenntnisse zu erneuerbaren Energien und energie- und umweltökonomische Einordnung
    4. Charakteristika von Wind und Solarenergie
    Literature
    • Endres, A. (2013). Umweltökonomie: Lehrbuch. W. Kohlhammer Verlag.
    • Sturm, B., & Vogt, C. (2011). Umweltökonomik: eine anwendungsorientierte Einführung. Springer-Verlag.
    • Kaltschmitt, M., Streicher, W., & Wiese, A. (2006). Erneuerbare Energien. Springer-Verlag Berlin Heidelberg.
    • Wokaun, A. (2013). Erneuerbare Energien. Springer-Verlag.
    Participants
    Lecture: Umweltökonomik und erneuerbare Energien (WIWI‑C1104)
    Name in diploma supplement
    Environmental Economics and Renewable Energy
    Organisational Unit
    Lecturers
    SPW
    2
    Language
    German
    Cycle
    winter semester
    Participants at most
    no limit
    Preliminary knowledge

    siehe Vorlesung

    Contents

    siehe Vorlesung

    Literature

    siehe Vorlesung

    Participants
    Exercise: Umweltökonomik und erneuerbare Energien (WIWI‑C1105)