Print Course syllabus
Course syllabus - AI-driven Decision Support Systems for Energy and Production Operations
Scope
3.0 credits
Course code
ERA323
Valid from
Spring semester 2025
Education level
Second cycle
Progressive Specialisation
A1N (Second cycle, has only first-cycle course/s as entry requirements)
Main area(s)
Energy Engineering
Organisation
School of Business, Society and Engineering
Ratified
2024-08-19
Literature lists
Course literature is preliminary up to 8 weeks before course start. Course literature can be valid over several semesters.
Objectives
The objective of this course is to develop understanding of practical AI solutions and learning systems in real life SCADA systems.
Learning outcomes
When people finish the course, they are expected to：
- Understand frameworks and concepts of SCADA system
- Understand concepts and models of learning system
- Know applied ML/DL in MPC, prediction and optimization
- How AI support decision making
Course content
- Introduction of SCADA System: framework, datastream, functions, models
- Introduction of Learning System: concepts, functions, models
- Prediction, Modeling and Optimization: machine learning and deep learning solutions
- Information Fusion for Decision Support
- Case Study
Specific requirements
75 credits in energy engineering, production technology, mechanical engineering, product and process development, computer technology and/or computer science or equivalent or 40 credits in technology and at least 2 years of full-time professional experience from a relevant area within industry. In addition, English A/English 6 are required
Examination
Home examination (HEM1) 1 credit, grade: Fail (U) eller Pass (G)
Assignment (INL1) 2 credit, grade: Fail (U) eller Pass (G)
A student who has a certificate from MDU regarding disability study support, can request adaptions for the examination. It is the examiner who takes decisions on any adaptions, based on the certificate and other conditions.
Grade
Two-grade scale