Number of hours
- Lectures 20.0
- Projects -
- Tutorials -
- Internship -
- Laboratory works 44.0
ECTS
ECTS 5.0
Goal(s)
Students will be able to:
- explain the new challenges posed by renewable energies, electric vehicles and energy storage for traditional power grids.
- identify the economic and reliability requirements that power grids must meet as part of a sustainable energy transition.
- describe the technologies and methods associated with Smart Grids and Smart Cities.
- analyze the contribution of intelligent systems to the control, optimization and supervision of modern power grids.
More specifically, he/she will be able to :
- explain the fundamental principles of machine learning and their application to energy systems
- implement and evaluate machine learning algorithms in a Smart Grid or Smart City context
- explain the key concepts related to Big Data and their importance in Smart Grid management
- exploit massive datasets to extract useful information for supervision or decision-making in Smart Grids
- describe how a SCADA system works and its role in power grid supervision.
- design or interpret SCADA architectures adapted to Smart Grid requirements
- identify and integrate home automation solutions within the framework of an intelligent building
- design energy management scenarios on a housing scale using home automation equipment
Benoit DELINCHANT
Content(s)
This unit (20hCM + 40hBE) consists of 4 modules:
• Machine learning (4h CM + 16h BE), 2 eval BE
• Big Data (8h CM + 4h BE), eval DS + eval BE
• Electrical grid supervision / SCADA (8h CM + 4h BE), eval DS + eval BE
• Home Automation (16h BE), eval BE
basic of data science, python programming and electrical engineering
Session normale / 1st session
Evaluation rattrapable (ER) / ER assessment: examen écrit de 2h / 2 hours of written exam on SCADA and Big Data courses
Session de rattrapage / 2nd session
La note obtenue remplace la note de ER. Le contrôle continu n'est pas rattrapable./ Another written exam will replace the first one (ER). No retake for EN.
Session 1 : ET 50 % + CC 50%
Session 2 : ET 50 % + CC 50%(report de S1)
The exam is given in english only
The course exists in the following branches:
- Curriculum - Master's Degree in Engineering SEM - Semester 9 (this course is given in english only
)
Course ID : 5EU9SSY1
Course language(s):
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