Syllabus for Information Technology and Energy Storage

Informationsteknik och energilagring

  • 5 credits
  • Course code: 1DT107
  • Education cycle: Second cycle
  • Main field(s) of study and in-depth level: Computer Science A1N

    Explanation of codes

    The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:

    First cycle

    • G1N: has only upper-secondary level entry requirements
    • G1F: has less than 60 credits in first-cycle course/s as entry requirements
    • G1E: contains specially designed degree project for Higher Education Diploma
    • G2F: has at least 60 credits in first-cycle course/s as entry requirements
    • G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
    • GXX: in-depth level of the course cannot be classified

    Second cycle

    • A1N: has only first-cycle course/s as entry requirements
    • A1F: has second-cycle course/s as entry requirements
    • A1E: contains degree project for Master of Arts/Master of Science (60 credits)
    • A2E: contains degree project for Master of Arts/Master of Science (120 credits)
    • AXX: in-depth level of the course cannot be classified

  • Grading system: Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
  • Established: 2022-03-02
  • Established by: The Faculty Board of Science and Technology
  • Applies from: Autumn 2022
  • 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.

  • Responsible department: Department of Information Technology

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.

Content

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.

Instruction

Lectures, seminars, and computer laboratory work.

Assessment

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.

Reading list

The reading list is missing. For further information, please contact the responsible department.

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