Subject

Machine Learning in Smart Energy Grids

Course Title

Machine Learning in Smart Energy Grids
Machine learning in smart grids

Code

m23_w_051

Study Programme

—

Organizer of the study programme (unit, institute, department or division)

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

Second cycle

Academic year / semester

9 / Winter

Number of ECTS credits

Teacher

Alexandra Dedinec

Prerequisites for enrolling in the course

—

10.

Objectives of the course programme (competences)

The goal of the course is to:
• претстават основните концепти на smart grid
• анализираат моменталните технологии во smart grid
• простудира примената на машинското учење врз електроенергетските системи
• анализираат методологии за дизајнирање на интелигентни мрежи

11.

Course content

Content
1. Што е Smart grid?
2. Основи на електроенергетски системи
3. Вовед во информациски и комуникациски технологии во smart grid
4. Машинско учење во smart grid
5. Методи за оптимизација и предвидување на дистрибуирани извори на енергија
6. Технологии за зачувување на енергија и оптимална интеграција на електрични возила
7. Demand side management and forecasting, demand response и demand pricing
8. Smart metering технологии.
9. Системи за подобрување на надежноста на дистрибутивната и преносната мрежа
10. Студии на случаи на smart grid

12.

Learning methods

NULL

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

45 + 15 + 30 + 50 + 40 = 180 hours

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	45 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	15 hours

16.

Other forms of activities

16.1.	Project assignments	50 hours
16.2.	Independent assignments	30 hours
16.3.	Home study	40 hours

17.

Assessment method

17.1.	Tests	45 points
17.2.	Seminar paper / project (presentation: written and oral)	50 points
17.3.	Activities and learning	10 points
17.4.	Final exam	0 points

18.

Grading criteria (points / grade)

up to 50 points	5 (five) (F)
from 51 to 60 points	6 (six) (E)
from 61 to 70 points	7 (seven) (D)
from 71 to 80 points	8 (eight) (C)
from 81 to 90 points	9 (nine) (B)
from 91 to 100 points	10 (ten) (A)

19.

Requirement for obtaining a signature and taking the final exam

Activities 15 and 16

20.

Language of instruction

NULL

21.

Method for monitoring the quality of teaching

NULL

22.

Literature

22.1.	Required literature 1. Salman K. Salman \| Introduction to the Smart Grid: Concepts, Technologies and Evolution \| Institution of Engineering and Technology \| 2017 2. Anish Jindal, Neeraj Kumar, Gagangeet Singh Aujla \| Internet of Energy for Smart Cities Machine Learning Models and Techniques \| CRC Press \| 2021
22.2.	Additional literature No. Author Title Publisher Year