inf5128 - AI in Energy Systems (Complete module description)

Module label	AI in Energy Systems
Module code	inf5128
Credit points	3.0 KP
Workload	90 h
Institute directory	Department of Computing Science
Applicability of the module	Master's Programme Computing Science (Master) > Angewandte Informatik Master's programme Digitalised Energy Systems (Master) > Innovation Topics and Smart Grids
Responsible persons	Bremer, Jörg (module responsibility) Lehrenden, Die im Modul (authorised to take exams)
Prerequisites	No participant requirements
Skills to be acquired in this module	The students learn to understand the energy system as self-organizing, self- optimizing and self-healing cyber physical system and how equip the components with of a cyber physical energy system with intelligence and autonomy Professional competences The students contrast different methods of AI define modern use cases of AI applications in energy systems identify appropriate AI methods to achieve a given control goal in the energy system evaluate risks and drawbacks of AI in energy systems apply AI to selected problems Methological competences The students examine tasks with technical and research literature, write an academic article and present their solutions academically evaluate problems of AI in energy systems schedule time processes and resources Social competences The students communicate with colleagues and experts convincingly Self competences The students pursue and reflect the integration of AI into energy systems critically reflect self-developed hypotheses to theories independently
Module contents	This module integrates current developments in artificial intelligence (AI) and its application to energy systems
Recommended reading	Will be announced in the corresponding course
Links
Language of instruction	English
Duration (semesters)	1 Semester
Module frequency	irregular
Module capacity	unlimited
Teaching/Learning method	V oder S

Examination	Prüfungszeiten	Type of examination
Final exam of module	at the end of the lecture period	term paper

Type of course	Course or seminar
SWS	2
Frequency	SuSe
Workload attendance time	28 h