Slides for: Machine Learning Applications in Microgrid Systems
Shashikant Madhukar Bakre
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Members: Free
IEEE Members: $11.00
Non-members: $15.00Pages/Slides: 22
01 Mar 2022
Learning Objectives –
1. to impart basic understanding of machine learning concept to audience .
2. To explain Python programming as a back end tool.
3. To explain applications of machine learning in Smart Microgrids
The session is broadly divided in three parts, as follows-
1. Part I-Review of Grid System, Microgrid, Smart Grid, Smart Microgrid.
2. Part II -Review of Machine Learning Concepts, Python programming and IoT
3. Part III-Applications of Machine Learning Tools in Microgrid – ANN based OR gate, SVM, K means Clustering, Decision tree, Linear Regression, Virtual Meter, Summation meters, Theft detection, Thermography.
Conclusion- The audience would learn about basic fundamentals of machine learning and their applications in Microgrids. They would be able to develop small applications based on this topic.
1. to impart basic understanding of machine learning concept to audience .
2. To explain Python programming as a back end tool.
3. To explain applications of machine learning in Smart Microgrids
The session is broadly divided in three parts, as follows-
1. Part I-Review of Grid System, Microgrid, Smart Grid, Smart Microgrid.
2. Part II -Review of Machine Learning Concepts, Python programming and IoT
3. Part III-Applications of Machine Learning Tools in Microgrid – ANN based OR gate, SVM, K means Clustering, Decision tree, Linear Regression, Virtual Meter, Summation meters, Theft detection, Thermography.
Conclusion- The audience would learn about basic fundamentals of machine learning and their applications in Microgrids. They would be able to develop small applications based on this topic.
