EG3131 Monte Carlo Methods in Electric Power Research 10.0 credits
Monte carlo-metoder inom elkraftforskning
The topic of this course is methods for solving complicated mathematical problems using sample surveys. Applications of Monte Carlo methods can be found in many fields, from opinion polls to simulation of technical systems. The focus of this course is going to be on Monte Carlo methods that are useful for research projects in electric power engineering.
Education cycleThird cycle
Main field of study
Grading scaleP, F
Autumn 18 P2 (10.0 credits)
Language of instruction
Form of study
Number of places
Information for research students about course offerings
Intended learning outcomes
After the course, the student should be able to
- state basic definitions concerning probability theory, random variables and sampling techniques,
- apply computation methods for random number generation, simple sampling and variance reduction techniques,
- formulate mathematical models appropriate for Monte Carlo simulation,
- analyse a simulation problem related to electric power research and design an efficient Monte Carlo simulation method for that problem.
Course main content
Definition of Monte Carlo simulation, random numbers, random number generation, simple sampling, complementary random numbers, dagger sampling, control variates, correlated sampling, importance sampling, stratified sampling, simulation design.
Lectures, home assignments, 4 h exam, project assignments.
The course is intended for Ph.D. students in electrical engineering, but can also be interesting for students from other fields of engineering.
Handouts, general textbooks, scientific publications.
- EXA1 - Examination, 10.0, grading scale: P, F
The project assignments are chosen by students in agreement with their supervisors and the examiner of the course.
Requirements for final grade
- Approved home assignments.
- Passed the exam.
- Approved project assignment.
EECS/Electric Power and Energy Systems
Course syllabus valid from: Autumn 2011.
Examination information valid from: Spring 2019.