Machine learning in smart grids
1. |
Subject title |
Machine learning in smart grids Машинско учење во паметни енергетски мрежи |
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2. |
Code |
m23_w_051 |
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3. |
Study program |
Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Еducation with ICT, Eco-informatics, Inteligent Systems, Internet Technologies and cyber security, Computer Science, Statistics and Data Analytics, Software Engineering, Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Statistics and Data Analytics, Software Engineering, Data science in computer science and engineering, Software for embedded systems, |
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4. |
Organizer of the study program (unit, institute, department, division) |
Faculty of Information Sciences and Computer Engineering |
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5. |
Study cycle (first, second, third) |
Втор циклус |
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6. |
Academic year / semester 5 / Зимски |
7. Number of ECTS credits 6.0 |
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8. |
Instructor |
ворн. проф. д-р Александра Дединец |
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9. |
Prerequisites for enrollment |
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10. |
Subject goals and competencies: The purpose of the course is to: • Present the basic concepts of Smart Grid • Analyze current technologies in Smart Grid • The application of machine learning to power systems will study • Analyze methodologies for designing intelligent networks
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11. |
Subject content: Content 1. What is Smart Grid? 2. Basics of power systems 3. Introduction to Information and Communication Technologies in Smart Grid 4. Machine learning in Smart Grid 5. Methods for SEO and predicting distributed energy sources 6. Energy conservation technologies and optimal integration of electric vehicles 7. Demand Side Management and Forecasting, Demand Response and Demand Pricing 8. Smart metering technologies. 9. Systems to improve the reliability of the distribution and transmission network 10. Studies of Smart Grid Cases |
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12. |
Learning methods: NULL |
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13. |
Total available time fund |
6.0 ECTS x 30 hours = 180 hours |
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14. |
Time distribution |
45 + 15 + 30 + 50 + 40 = 180 hours
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15. |
Forms of teaching activities |
15.1. |
Lectures - theoretical teaching |
45 hours |
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15.2. |
Exercises (laboratory, classroom), seminars, team work |
15 hours |
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16. |
Other forms of activities |
16.1. |
Project tasks |
50 hours
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16.2. |
Independent tasks |
30 hours |
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16.3. |
Homework |
40 hours |
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17. |
Grading method |
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17.1. |
Tests |
45 points |
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17.2. |
Seminar work / project (presentation: written and oral) |
50 points |
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17.3. |
Activities and learning |
10 points |
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17.4. |
Final exam |
0 points |
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18. |
Grading criteria (points / grade) |
up to 50 points |
5 (five) (F) |
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from 51 to 60 points |
6 (six) (E) |
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from 61 to 70 points |
7 (seven) (D) |
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from 71 to 80 points |
8 (eight) (C) |
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from 81 to 90 points |
9 (nine) (B) |
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from 91 to 100 points |
10 (ten) (A) |
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19. |
Condition for signature and taking final exam |
реализирани активности 15 и 16 |
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20. |
Language of instruction |
NULL |
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21. |
Quality assurance method |
NULL
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22. |
Literature |
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22.1. |
Mandatory literature |
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22.2. |
Additional literature |
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