inf514 Simulation-based Smart Grid Engineering and Assessment (Course overview)

inf514 Simulation-based Smart Grid Engineering and Assessment (Course overview)

Department of Computing Science 6 KP
Module components Semester courses Winter semester 2024/2025 Examination
Lecture
  • No access 2.01.514 - Simulation-based Smart Grid Engineering and Assessment Lehrende anzeigen
    • Jörg Bremer
    Termine anzeigen
    • Tuesday, 10:00 - 12:00, Weekly (from 15.10.24)
    • Thursday, 12:00 - 14:00, Weekly (from 17.10.24)
    • Thursday, 13.02.25, 10:00 - 14:00 o'clock
    • Thursday, 06.03.25, 11:00 - 14:00 o'clock
    • Monday, 10.03.25, 10:00 - 14:00 o'clock
    • Thursday, 20.03.25, 10:00 - 14:00 o'clock
    • Thursday, 27.03.25, 10:00 - 14:00 o'clock
    • Friday, 11.04.25, 10:00 - 14:00 o'clock
    • Wednesday, 16.04.25, 10:00 - 14:00 o'clock

Exercises
  • No access 2.01.514 - Simulation-based Smart Grid Engineering and Assessment Lehrende anzeigen
    • Jörg Bremer
    Termine anzeigen
    • Tuesday, 10:00 - 12:00, Weekly (from 15.10.24)
    • Thursday, 12:00 - 14:00, Weekly (from 17.10.24)
    • Thursday, 13.02.25, 10:00 - 14:00 o'clock
    • Thursday, 06.03.25, 11:00 - 14:00 o'clock
    • Monday, 10.03.25, 10:00 - 14:00 o'clock
    • Thursday, 20.03.25, 10:00 - 14:00 o'clock
    • Thursday, 27.03.25, 10:00 - 14:00 o'clock
    • Friday, 11.04.25, 10:00 - 14:00 o'clock
    • Wednesday, 16.04.25, 10:00 - 14:00 o'clock

Hinweise zum Modul
Prerequisites

Basic programming in Java or Python

Further information

Module Capacity:
unrestricted

Method of assesment

Written exam or oral exam

Exam repetition information :

Repeat examination before the beginning of the course period of the following semester

Weitere Prüfungsmodalitäten

Repeat examination before the beginning of the course period of the following semester

Exam dates

At the end of the lecture term

Learning outcomes/competencies

Goal of this module is to teach mathematical and methodological foundations of energy informatics and for conducting large-scale simulation studies
Professional competence
The students:

  • know methods to analyze black-box objective functions
  • recognize the relation between precision and reliability of expected results and the necessary surplus effort
  • know methods to determine cause-effect relations between input parameters with small numbers of simulations (experiments)
  • evaluate the significance of simulation results
  • characterize (distributed) algorithms by their properties
  • transfer proving techniques to distributed problems


Methological competence
The students:

  • choose suitable statistical methods to interpret simulation results
  • apply methods from design of experiments
  • apply significance tests to compare algorithms
  • generate arbitrarily distributed input data
  • present results from algorithm evaluation statistically sound


Social competence
The students:

  • discuss the own algorithm choice
  • present their results and discuss with other students


Self-competence
The students:

  • reflect their own usage of the scarce resource energy
  • reflect problems and uncertainties when using statistical methods
  • recognize the limits of simulation studies and their responsibility for choosing correct statistical methods
  • accept criticism and understand it as a suggestion for the further development of their own actions