Moses - Smart Energy Systems

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Diese Modulbeschreibung gilt nicht im aktuellen Semester.

Die Modulbeschreibung gilt nur für den Zeitraum SS 2019 - SS 2019.

Module / Version:
#40916 / #2

Validity:
SS 2019 - SS 2019

Default display language:
English

Module title:
Smart Energy Systems

Credits:
9

Responsible person:
Albayrak, Sahin

Grading:
benotet

Type of exam:
Portfolioprüfung

Zugehörigkeit

Faculty:
Fakultät IV

Institute:
Institut für Wirtschaftsinformatik und Quantitative Methoden

Area of expertise:
34361200 FG Agententechnologien in betrieblichen Anwendungen und der Telekommunikation (AOT)

Prüfungsausschuss:
No information

Kontakt

Office:
TEL 14

Contact person:
Yurdakul, Ogün

E-mail address:
sahin.albayrak@tu-berlin.de

Website:

https://www.aot.tu-berlin.de/index.php?id=2895

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

Content

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

Pflichtgruppe:

All Courses are mandatory.

Course Name	Type	Number	Cycle	Language	SWS	VZ
Smart Energy Systems	PJ	0435 L 779	WiSe/SoSe	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:

This module has no requirements.

Module completion

Grading

graded

Type of exam

Portfolio examination

Type of portfolio examination

100 Punkte insgesamt

Language

German/English

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

Notenschlüssel »Notenschlüssel 2: Fak IV (2)«

Gesamtpunktzahl	1.0	1.3	1.7	2.0	2.3	2.7	3.0	3.3	3.7	4.0
100.0pt	95.0pt	90.0pt	85.0pt	80.0pt	75.0pt	70.0pt	65.0pt	60.0pt	55.0pt	50.0pt

Test description (Module completion)

No information

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:
Winter- und Sommersemester.

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 literature
No recommended literature given

Assigned Degree Programs

Semester:

Zeige auch ausgelaufene Studiengänge und StuPOs

This module is used in the following Degree Programs (new System):

	Studiengang / StuPO	StuPOs	Verwendungen	Erste Verwendung	Letzte Verwendung
This module is not used in any degree program.

Miscellaneous