| Course Name | Intelligent Energy Management Systems |
| Course Code | 23AID465 |
| Program | B.Tech in Artificial Intelligence and Data Science |
| Credits | 3 |
| Campus | Coimbatore , Amritapuri ,Faridabad , Bangaluru, Amaravati |
Introduction to Energy Management:Overview – Types of energy management systems – Key components of intelligent energy management systems – Types of controllers and algorithms used in energy management systems – Algorithms for energy management and optimization – Data analytics techniques for energy management.
Energy Efficiency:Concepts of energy efficiency – Techniques for improving energy efficiency – Measurement and verification of energy savings using data analytics.
Demand Response:Introduction – Techniques for implementing demand response programs – Data-driven approaches to demand response.
Renewable Energy Integration & Storage:Challenges and opportunities for renewable energy integration with data-driven approaches – Overview of energy storage technologies – Integration of energy storage into energy management systems – Benefits and challenges of energy storage with data-driven approaches.
Course Objectives
Course Outcomes
After completing this course, students will be able to
|
CO1 |
Identify and explain the key components of intelligent energy management systems and their application in optimizing energy consumption, using data analytics tools and techniques |
|
CO2 |
Analyse energy usage data using various data analytics techniques to identify opportunities for energy savings and efficiency improvements and develop recommendations for implementation. |
|
CO3 |
Evaluate the benefits and challenges of implementing intelligent energy management systems in buildings and other applications, using quantifiable metrics and data-driven approaches |
|
CO4 |
Formulate simple intelligent energy management systems, using data analytics tools and techniques to optimize energy consumption and efficiency. |
CO-PO Mapping
|
PO/PSO |
PO1 |
PO2 |
PO3 |
PO4 |
PO5 |
PO6 |
PO7 |
PO8 |
PO9 |
PO10 |
PO11 |
PO12 |
PSO1 |
PSO2 |
PSO3 |
|
CO |
|||||||||||||||
|
CO1 |
2 |
1 |
– |
1 |
– |
1 |
2 |
– |
– |
– |
– |
1 |
1 |
– |
1 |
|
CO2 |
2 |
3 |
2 |
2 |
2 |
1 |
2 |
– |
– |
– |
1 |
2 |
2 |
– |
2 |
|
CO3 |
2 |
2 |
2 |
2 |
2 |
1 |
3 |
– |
– |
– |
1 |
2 |
3 |
– |
3 |
|
CO4 |
3 |
3 |
3 |
3 |
2 |
1 |
2 |
– |
– |
– |
1 |
2 |
3 |
– |
3 |
Evaluation Pattern
|
Assessment |
Internal/External |
Weightage (%) |
|
Assignments (minimum 2) |
Internal |
30 |
|
Quizzes (minimum 2) |
Internal |
20 |
|
Mid-Term Examination |
Internal |
20 |
|
Term Project/ End Semester Examination |
External |
30 |
Text Books / References
Craig B. Smith and Kelly Parmenter, “Energy Management Principles, Second Edition”,2015
James Momoh, “Smart Grid: Fundamentals of Design and Analysis”.2012
Lawrence E. Jones, “Renewable Energy Integration: Practical Management of Variability, Uncertainty, and Flexibility in Power Grids”,2017
Pengwei Du and Ning Lu, “Energy Storage for Smart Grids: Planning and Operation for Renewable and Variable Energy Resources (VERs)”,2014
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