AI-driven Decision Support Systems for Energy and Production Operations
This course explores the integration of artificial intelligence (AI) in decision support systems specifically tailored for the energy and production sectors. You will learn how AI technologies, such as machine learning, optimization, and data analytics, are transforming traditional operational strategies, enhancing decision-making processes, and driving efficiency in energy and production operations.
The course covers foundational concepts of AI and decision support systems, along with practical applications such as predictive maintenance, demand forecasting, process optimization, and real-time decision support. Through hands-on projects, case studies, and industry-relevant examples, you will gain insight into designing and implementing AI-driven solutions that improve operational performance, reduce costs, and support sustainability goals.
By the end of the course, you will be equipped with the skills to develop and apply AI-driven decision support systems to solve complex challenges in energy and production environments. This course is ideal for professionals and students interested in leveraging AI for operational excellence in the energy and production industries.
Occasions for this course
-
Scope
3 credits
Time
2025-01-20 - 2025-03-30 (part time 25%)
Education level
Second cycle
Course type
Freestanding course
Application code
MDU-13057
Language
English
Study location
Independent of location
Teaching form
Distance learning
Number of mandatory occasions including examination: 0
Number of other physical occasions: 0Course syllabus & literature
See course plan and literature list (ERA323)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
Selection
University credits
Spring semester 2026
-
Scope
3 credits
Time
2026-01-19 - 2026-03-29 (part time 25%)
Education level
Second cycle
Course type
Freestanding course
Application code
MDU-13057
Language
English
Study location
Independent of location
Teaching form
Distance learning
Number of mandatory occasions including examination: 0
Number of other physical occasions: 0Course syllabus & literature
See course plan and literature list (ERA323)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
Selection
University credits