Information Technology and Energy Storage

5 credits

Syllabus, Master's level, 1DT107

Education cycle
Second cycle
Main field(s) of study and in-depth level
Computer Science A1N
Grading system
Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
Finalised by
The Faculty Board of Science and Technology, 2 March 2022
Responsible department
Department of Information Technology

Entry requirements

120 credits including 90 credits in computer science, chemistry, mathematics, and technology. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Learning outcomes

On completion of the course, the student should be able to:

- report for the energy profile for Internet of Things (IoT) applications, wireless systems, and other emerging technologies.

- explain and motivate use cases in which artificial intelligence tools can be used in the field of chemical energy storage.

- explain and compare basic machine learning methods in the context of modeling energy storage.

- use machine learning techniques and software to model energy storage.


Introduction to recent digitalization concepts of technological importance: Internet of Things, wireless communication systems, and its interrelation with energy storage. Introduction to machine learning and artificial intelligence: its terminology, an overview of basic algorithms and literature study on its use in modeling energy storage. Use of established tools and algorithms for machine learning in modeling energy storage.


Lectures, seminars, and computer laboratory work.


Written examination (2 credits), oral seminar (1 credit), and laboratory work (2 credits). 

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

No reading list found.