Smart Systems - 5EU9SSY1

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

Responsible(s)

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

Test

1st session
Final assessment (ET1) : 2 hours of written exam on SCADA and Big Data courses

2nd session
Another written exam will replace the first one ET1. No retake for CC1.

The exam is given in english only

Calendar

The course exists in the following branches:

• Curriculum - Master's Degree in Engineering SEM - Semester 9 (this course is given in english only )

Additional Information

Course ID : 5EU9SSY1
Course language(s):

You can find this course among all other courses.