IN9460 ¨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, and gained insight about the corresponding research challenges.
• analyzed and developed an understanding of which techniques/algorithms can be applied to solve what 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.
• gained experience in presenting a lecture to Master level students.

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?appl