About this course...
The goal of the Smart Grid is to provide an information-centric framework that enables efficient, secure, reliable, sustainable and interoperable operation of the power grid. To facilitate this, a multitude of measurement, status or third party data is collected from various heterogeneous sources in different formats and at various intervals. The sheer volume of data, the rate at which it is collected, and its variety introduce significant challenges for the entities involved in processing and ultimately utilizing it. Data analytics tries to address this problem by introducing solutions to describe the data and identify its underlying patterns, to predict the potential outcomes, and finally to prescribe appropriate decisions to be made.
This 2½-day short course provides an advanced understanding of data analytics as it relates to the modern power and energy systems. Upon the completion of this course, the participants will have a clear understanding of the techniques for statistical analysis, forecasting, and machine learning as it relates to the Smart Grid. Numerous use cases will be presented and studied in order to incorporate the concepts covered in lectures into the context of energy systems. The lectures are designed with the goal of providing the right balance between theory and practice in order to maximize their value and effectiveness for a broader range of audience. An overview of the Hadoop Distributed File System, as a tool for managing large data sets, will also be provided.
Who should attend?
This course is intended for managers, researchers, educators, utility engineers and consultants who are interested in or work in the field of data analytics as related to power and energy systems. Various case studies presented in the course will benefit those involved in energy forecasting, system planning, as well as energy marketing. The course provides an excellent opportunity to learn, network and explore new possibilities.
The course program highlights topics that will be covered in the course. Learn more...
Continuing education credits
Colorado School of Mines will award 1.65 Continuing Education Credits (CEUs) to participants who complete this course.
The course will be taught on the campus of Colorado School of Mines in Golden, Colorado USA. Learn more...
Additional information about Golden is available. Learn more...
Registration for this course is open now. Enrollment is limited; therefore, applications will be accepted in the order received. Full information about fees, options, and payment methods is available. Learn more...
Travel and accommodations
Registrants are responsible for their own travel arrangements, transportation, lodging, and meals. Additional information is available through the links below.
The course will be taught by Dr. Salman Mohagheghi of the Electrical Engineering and Computer Science Department at Colorado School of Mines. With several years of industry experience as a Senior R&D Engineer with ABB Corporate Research, Dr. Mohagheghi combines industry practices with the latest theoretical advances in power grid management, machine learning, and optimization in order to devise solutions for automation, security, and situational awareness in the modern grid. Dr. Mohagheghi is a Senior Member of the IEEE. Since 2009, he has been the ANSI representative at the IEC Technical Committee 57 Working Group 17: "Power System IED Communication and Associated Data Models for Distributed Energy Resources and Distribution Automation".
Further technical information
For more information about the course content, please contact:
Dr. Salman Mohagheghi
Electrical Engineering Department
Colorado School of Mines
Receive notification by email of upcoming events of interest to you.