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Smart Energy Systems

9 LP


#40916 / #2

SS 2019 - SS 2019

Fakultät IV

TEL 14

Institut für Wirtschaftsinformatik und Quantitative Methoden

34361200 FG Agententechnologien in betrieblichen Anwendungen und der Telekommunikation (AOT)

Albayrak, Sahin

Draz, Mahmoud Alsayed Alsayed

POS-Nummer PORD-Nummer Modultitel
2348273 40083 Smart Energy Systems

Learning Outcomes

(At the core of this course is the development of an intelligent and autarkic energy supply for small and medium-sized prosumers) By the end of this course, students should be able to: • Identify suitable machine learning techniques for specific forecasting goals (e.g. household power consumption and generation behavior); • Implement machine learning software modules (preferably in Python or Matlab); • Identify elements of a smart energy system and carry out basic energy calculations; • Formulate and solve optimization problems for micro energy systems; • Develop smart solutions for integrating distributed generation (DG) and electric vehicles (EVs) into the power grid; and • Apply the knowledge gained to real-life energy systems


This course covers a multidisciplinary space between information technology and energy engineering. It consists mainly of two parts. The first part is formulated as a series of lectures in which theoretical materials are provided. These lectures cover fundamentals such as the energy supply chain, an introduction to distributed energy generation, different optimization methods, and machine learning (ML) techniques. The optimization methods are used to optimally control an energy system, and the ML techniques are employed to forecast the load and generation behaviour of a household. The second part of the course will be devoted to group work. In this part, students will be divided into teams, and each team will pick a topic. This topic should be an example of applying the methods and techniques taught in the first part. While the students work in teams, regular support sessions will be held biweekly. At the end of the semester, each team will present its results and key findings to the other teams.

Module Components


All Courses are mandatory.

Course Name Type Number Cycle Language SWS
Smart Energy Systems PJ 0435 L 779 WS/SS German/English 4

Workload and Credit Points

Smart Energy Systems (PJ):

Workload description Multiplier Hours Total
Attendance 15.0 4.0h 60.0h
Exam Preparation 1.0 20.0h 20.0h
Implementation 1.0 80.0h 80.0h
Pre/post processing 15.0 2.0h 30.0h
Presentation Preparation 1.0 20.0h 20.0h
Project Paper 1.0 40.0h 40.0h
Reflection Paper 1.0 20.0h 20.0h
270.0h (~9 LP)
The Workload of the module sums up to 270.0 Hours. Therefore the module contains 9 Credits.

Description of Teaching and Learning Methods

Students are expected to attend lectures. They will also take part in teamwork projects and practice in regular support sessions.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Students are expected to have good programming skills.

Mandatory requirements for the module test application:

No information

Module completion



Type of exam:

Portfolio examination



Typ of portfolio examination

100 points in total

Test elements

Name Points Categorie Duration/Extent
Exam 18 oral 20 minutes
Implementation 40 practical 12 weeks
Presentation 10 oral 30 minutes
Project paper 22 written 10 pages
Reflection paper 10 written 2 pages

Grading scale

Test description (Module completion)

No information

Duration of the Module

This module can be completed in one semester.

Maximum Number of Participants

The maximum capacity of students is 20.

Registration Procedures

Enrolment is done via Qispos or examination office (Prüfungsamt) and additionally registration on corresponding ISIS course page.

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable

Electronical lecture notes

Availability:  available


Recommended literature
No recommended literature given.

Assigned Degree Programs

This module is used in the following modulelists:


No information