IN9410 – Energy Informatics
Changes in the course due to coronavirus
Autumn 2020 we plan for teaching and examinations to be conducted as described in the course description and on semester pages. However, changes may occur due to the corona situation. You will receive notifications about any changes at the semester page and/or in Canvas.
Spring 2020: Teaching and examinations was digitilized. See changes and common guidelines for exams at the MN faculty spring 2020.
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.
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.
Recommended previous knowledge
The course assumes basic informatics knowledge.
- 10 credits overlap with IN5410 – Energy Informatics.
- 10 credits overlap with INF5870 – Energy Informatics (continued).
- 10 credits overlap with INF9870 – Energy Informatics (continued).
3 hours of lectures, seminars and guest lectures per week.
There are mandatory programming assignments in the course.
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 Informatics, INF5870 - Energy Informatics (continued), INF9870 - Energy Informatics (continued)
Examination support material
No examination support material is allowed.
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.