Artificial intelligence in energy technology
Max amount of FITech students: 15
On this course, students will learn the principles of fuzzy logic, rules and control, evolutionary computation, multiparameter and global optimisation as well as basics of neural networks. The idea is to learn how to apply these theoretical methods to energy applications. Content also includes different soft computing methods and designing, implementing and testing simple soft computing applications.
After completing this course, the student can
- explain and model uncertain information and the principles of fuzzy logic and fuzzy reasoning
- describe the most important energy applications and application areas of fuzzy logic
- apply the principles of fuzzy sets theory, fuzzy rules and fuzzy control
- explain the principles of neural networks and describe the most important neural network types
- apply learning of neural networks
- describe the most important energy applications of neural networks
- describe the principles of evolutionary computation
- apply the principles of multiparameter optimisation
- describe the principles of global optimisation
- describe the typical energy applications of genetic algorithms
- implement an application of genetic algorithms
- combine and apply different soft computing methods
- design, implement, test and document a simple soft computing application.
Terminology in Finnish and in English.
Course develops verbal representation (lecture), literal representation (documentation in English with Finnish abstract), coopertion skills (group lecture and labwork), lifelong learning (critical search for information and anlysis), IT skills (problem solving by programming and computing).
The course has a project work (ICAT2092) and an exam (ICAT2091) on Sat 28.3. at 12–15.
More information on University of Vaasa’s WebOodi course page.
You can get a digital badge after completing this course.
Contact person for applications