IN5460 ¨C Artificial Intelligence for Energy Informatics

Course content

The course provides an introduction to the application of artificial intelligence techniques and algorithms for data driven intelligent decision making in the area of smart and sustainable energy and transport systems of the future. Major focus will be on the application of deep learning and advanced machine learning techniques. Topics covered in the course include fundamental concepts related to smart and sustainable energy and transport systems, such as demand response, energy management, energyinformatics, electric mobility and energy efficiency, and applications of deep learning algorithms and advanced machine learning concepts to solve typical decision making problems in this area.

Learning outcome

After having taken this course you will have:

• knowledge about smart and sustainable energy and transport systems such as smart grid, electric vehicles and internet of vehicles.
• learnt about fundamental concepts and techniques/algorithms related to deep learning and advanced machine learning - e.g., recurrent neural networks, convolutional neural networks, deep reinforcement learning, federated learning, generative adversarial networks, etc.
• knowledge about typical data driven intelligent decision making problems for smart and sustainable energy and intelligent transport systems.
• analyzed and developed an understanding of which techniques/algorithms can be applied to solve which kind of problems in future smart and sustainable energy and intelligent transport systems.
• learnt using software tools and real data sets to solve some of those problems, and understood how the performance of different techniques/algorithms compare.
• met invited speakers from industry, and from their lectures experienced how academic concepts and algorithms are deployed in real systems.

Admission to the course

Students who are admitted to study programmes at UiO must each semester register which courses and exams they wish to sign up for?in Studentweb.

Recommended previous knowledge

Students ought to have basic knowledge of ICT and networks, e.g. from IN2010 and IN3230.

It will also be advantageous to have experience with linear algebra, e.g. from MAT1120.

Overlapping courses

• 10 credits overlap with IN9460 ¨C Artificial Intelligence for Energy Informatics.

Teaching

3 hours of lectures, seminars and guest lectures per week. The course has?mandatory assignments that must be approved prior to the exam.

Read more about requirements for submission of assignments, group work and legal cooperation under guidelines for mandatory assignments.

Examination

Oral Exam.

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: IN9460 ¨C Artificial Intelligence for Energy Informatics

Grading scale

Grades are awarded on a scale from A to F, where A is the best grade and F?is a fail. Read more about?the grading system.

More about examinations at UiO

• Use of sources and citations
• Special exam arrangements due to individual needs
• Withdrawal from an exam
• Illness at exams / postponed exams
• Explanation of grades and appeals
• Resitting an exam
• Cheating/attempted cheating

You will find further guides and resources at the web page on examinations at UiO.