Einzelansicht eines Moduls
Modul (6 Credits)
Energy Forecasting Competition
- Name im Diploma Supplement
- Energy Forecasting Competition
- Verantwortlich
- Voraussetzungen
- Siehe Prüfungsordnung.
- Workload
- 180 Stunden studentischer Workload gesamt, davon:
- Präsenzzeit: 60 Stunden
- Vorbereitung, Nachbereitung: 100 Stunden
- Prüfungsvorbereitung: 20 Stunden
- Dauer
- Das Modul erstreckt sich über 1 Semester.
- Qualifikationsziele
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
- Praxisrelevanz
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.
- Prüfungsmodalitäten
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).
- Verwendung in Studiengängen
- Bestandteile
Vorlesung mit integriertem Seminar (6 Credits)
Energy Forecasting Competition
- Name im Diploma Supplement
- Energy Forecasting Competition
- Anbieter
- Lehrperson
- SWS
- 4
- Sprache
- englisch
- Turnus
- unregelmäßig
- maximale Hörerschaft
- unbeschränkt
- empfohlenes Vorwissen
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.
- Lehrinhalte
- Introduction on forecasting competitions
- Competition design and reporting of forecasts
- Evaluation metrics
- Benchmark methods
- Options for improving forecasts
- 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. 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.
- didaktisches Konzept
Classic lectures + Learning by doing
Die Veranstaltung entspricht einem Vorlesungsanteil von 2 SWS und einem Seminaranteil von 2 SWS.
- Hörerschaft