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Teaching and exams spring 2022

In light of the most recent infection control regulations, we will at the start of the spring semester 2022 increase our online teaching, while we at the same time try to maintain in-person teaching where this is possible. We hope to go back to more in-person teaching later on in the semester. You will be informed about any changes in teaching or examinations on the semester page, in Canvas or through your regular channels.

Read more about postponed exams for the autumn semester 2021.

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 machine learning, cloud computing, fog computing, Blockchain, data center, 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., machine learning, cloud computing, fog computing, Blockchain, data center, 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


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

There are mandatory programming assignments in the course.


The exams in the course consist of the following:

  • 2 project assignments. Each assignment is worth 15% of the final grade.
  • Oral exam, which is worth 70% of the final grade.

All parts of the exams must be passed, and must be passed in the same semester.

All mandatory assignments must be approved before you can take the 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) Jan. 16, 2022 11:28:12 PM

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