Schedule, syllabus and examination date

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Changes in the course due to coronavirus

Autumn 2020 the exams of most courses at the MN Faculty will be conducted as digital home exams or oral exams, using the normal grading scale. The semester page for your course will be updated with any changes in the form of examination.

See general guidelines for examination at the MN Faculty autumn 2020.

Course content

The course provides an introduction to how informatics methods, techniques and tools can contribute to creating the sustainable energy systems of the future. Topics covered include cloud computing, big data, machine learning, game theory and optimization and their application in different kinds of energy systems such as smartgrids with integrated solar and wind power, energy storage and electric vehicles.

Learning outcome

After having taken this course you have:

  • knowledge about different energy systems - e.g., smart grid, electric vehicles, vehicle-to-grid, storage, transport, buildings
  • knowledge about renewable energy resources - e.g., solar and wind, and their impacts on energy systems
  • an understanding for smart grid concepts & components, including smart meters, advanced metering infrastructure, information networks, demand response, and pricing schemes
  • an understanding of where and how computer science techniques - e.g., cloud computing, fog computing, 5G, software defined networking, big data, game theory, optimization, apply for future sustainable energy systems
  • acquired deeper knowledge about optimization and machine learning principles
  • learned how to model power systems with software tools and real data sets to assess impact of smart grid concepts, integration of renewable resources, storage and electric vehicles
  • met invited speakers from industry and understood the good connection between principles and their applications in real systems

Admission to the course

PhD candidates from the University of Oslo should apply for classes and register for examinations through Studentweb.

If a course has limited intake capacity, priority will be given to PhD candidates who follow an individual education plan where this particular course is included. Some national researchers’ schools may have specific rules for ranking applicants for courses with limited intake capacity.

PhD candidates who have been admitted to another higher education institution must apply for a position as a visiting student within a given deadline.

The course assumes basic informatics knowledge.

Overlapping courses

Teaching

3 hours of lectures, seminars and guest lectures per week.

There are mandatory programming assignments in the course.

Examination

Oral exam.

It will also be counted as one of your three attempts to sit the exam for this course, if you sit the exam for one of the following courses: IN5410 - Energy InformaticsINF5870 - Energy Informatics (continued)INF9870 - Energy Informatics (continued)

Examination support material

No examination support material is allowed.

Grading scale

Grades are awarded on a pass/fail scale. Read more about the grading system.

Resit an examination

Students who can document a valid reason for absence from the regular examination are offered a postponed examination at the beginning of the next semester. Re-scheduled examinations are not offered to students who withdraw during, or did not pass the original examination.

Special examination arrangements, use of sources, explanations and appeals

See more about examinations at UiO

Last updated from FS (Common Student System) Nov. 27, 2020 10:17:32 PM

Facts about this course

Credits
10
Level
PhD
Teaching
Spring
Examination
Spring
Teaching language
English