Machine Learning for Power Engineers
After participating in this course, you will have an understanding of:
- The basics of machine learning algorithms.
- The overall implementation of machine learning from collecting the data to the classification process.
- Sources of machine learning errors.
- New trends in machine learning.
- The use of available machine learning software.
- Application of machine learning to different power engineering problems.
Application of machine learning in the power system is an integral part of the smart grid. This course introduces the basic knowledge of machine learning for power engineers with emphasis on applications related to the power industry. Types of machine learning algorithms like supervised, unsupervised and deep learning will be discussed. Also, the practical problems that diminish the efficiency of machine learning like over fitting and imbalance of data will be highlighted.
The main focus of the course is on the practical application of machine learning to the power grid without going deep into the math or the statistics behind the different algorithms. Hands-on tutorials using a free software will be utilized to demonstrate the application of machine learning to the power industry.
- Examples of machine learning applications in power engineering
- Problems with machine learning and their remedies
- New trends in machine learning
- Introduction to FREE commercial machine learning software
- Hands-on study cases (using WEKA)
Who Should Attend
This course is intended as an introduction for machine learning and its application to the power industry for power managers and engineers who are dealing with different parts of the power grid. The course does not require any background in programming, math, or statistics.
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Almost all of EPIC's courses offer :
- Continuing Education Units (CEUs) and
- Professional Development Hours (PDHs)
These course credits will help attendees earn training requirements for their associations or provincial governing bodies.
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