Artificial Intelligence in Energy Systems


Content, progress and pedagogy of the module

Programming experience in MATLAB or Python is recommended.

Learning objectives


  • Have knowledge about the progress and state-of-the-art of AI-based applications in energy sector
  • Have knowledge about basic machine/deep learning approaches and how to apply them (supervised learning, unsupervised learning, reinforcement learning, anomaly detection)
  • Have knowledge about different machine learning models and relevant variants (ANN, decision trees, SVM (Support Vector Machine), …)
  • Have knowledge about typical data-driven implementation procedures including data collection and pre-processing techniques, with end-to-end toolbox creating good training data, feature extraction


  • Be able to use different machine learning approaches in electrical engineering applications
  • Be able to train machine learning models and prepare training data
  • Be able to analyse and understand the training results
  • Be able to apply cutting-edge data-driven toolboxes for field applications.


  • Be able to have a data-driven thinking and know when and why using AI for energy applications
  • Be able to define a machine learning problem, gather the relevant data
  • Be able to implement the machine learning models and use fundamental libraries for machine learning
  • Be able to understand how to process training data and analyse the obtained model output

Type of instruction

The course is taught by a mixture of lectures, exercises, and e-learning activities.

Extent and expected workload

Since it is a 5 ECTS course module, the work load is expected to be 150 hours for the student.



Name of examArtificial Intelligence in Energy Systems
Type of exam
Oral exam
The exam is based on a report submitted by individual students or small groups of students.
Permitted aids
With certain aids:
For more information about permitted aids, please visit the course description in Moodle.
Assessment7-point grading scale
Type of gradingInternal examination
Criteria of assessmentThe criteria of assessment are stated in the Examination Policies and Procedures

Facts about the module

Danish titleKunstig intelligens i energisystemer
Module codeN-EE-K3-27
Module typeCourse
Duration1 semester
Language of instructionEnglish
Empty-place SchemeYes
Location of the lectureCampus Aalborg
Responsible for the module


Study BoardStudy Board of Energy
DepartmentDepartment of Energy
FacultyThe Faculty of Engineering and Science