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Learning Objectives

What you will learn: 

Discussions and computer-based simulation projects will prepare the participants to understand better how to integrate data-driven and physics-based reasoning in modern power systems. 

Topics

• Grid Operation Basics 

• Intro to Data Availability in Power Systems 

• Machine Learning (ML) 

• Neural Networks 

• Deep Learning

• Unsupervised Learning for Power Systems 

• Reinforcement Learning

• Large Language Models (LLMs)

• Integration and System Design 

• Application of AI and ML for Grid Operations 

• Application of AI and ML for Grid Planning

Registration Overview

  • This course is delivered face to face with total hours of 21 (CEU 2.1, PDH 21)
  • 25% discount will be available to SGC members company employees.  Please contact Victoria Schmidt for details.
  • Current TAMU Engineering Grad Students email Victoria Schmidt prior to registry for discount code.

Please direct questions about the offering to Jonathan Snodgrass. For questions related to the registration process, please contact TEES EDGE.

Target Audience

This course is ideally suited for those who work in areas associated with the electric grid and need to better understand the latest advance in data sciences and machine learning and how their work might be affected by this change.

Registration Fees and Payment Details

  • Course fee is $1,795.00 per person (non-refundable)
  • Credit Card or Electronic Check - Please have payment ready to apply during registration.
  • Request Approval for Invoicing
  • Payment for the course must be received prior to the course start date.