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Course Overview

Course Topics

Short course topics include leveraging multi-scale data from synchrophasors, smart meters, weather, and electricity markets; exploring the operational carbon footprint of AI computing; optimizing datacenter networks for energy efficiency; and integrating environmentally responsible AI solutions. 

This professional training course introduces the foundational concepts of high-dimensional spaces, data analytics, and sustainable computing practices necessary to model and operate modern power systems and datacenter networks. 

Course Format

This is an in-person course including: 

  • Lectures
  • Case discussions 
  • Hands-on experience with tools and models

Course Outcomes

Participants will gain hands-on experience with tools for statistical time series analysis, dimensionality reduction, and energy-efficient AI solutions. We will explore the differences between first-principle models, data-driven models, and sustainable AI strategies in real-time operations, with discussions and computer-based simulation projects. These activities will help participants integrate data-driven and physics-based reasoning while considering the environmental impact of AI and computing infrastructures. 

Power systems engineers: This course will explore the demands, constraints, and flexibility of data center workloads.

Data center device vendors: this course will help understand and increase the value of their design knobs for data centers in the energy market. 

Data center operators: this course will help explore scheduling strategies that would allow faster integration to the grid, as well as faster construction of data centers.

Learners who attend the complete course will receive a certificate from Harvard John A. Paulson School of Engineering and Applied Sciences.

Course Learning Objectives

  • Analyze the unique challenges and opportunities presented by the integration of AI and machine learning within modern power systems, particularly in the context of a transitioning energy landscape.
  • Apply foundational concepts of data analytics, high-dimensional spaces, and sustainable computing practices to effectively model, optimize, and operate contemporary power systems and data center networks.
  • Synthesize knowledge of first-principle models, data-driven approaches, and sustainable AI strategies to develop innovative solutions for real-time power system operation while considering both performance and environmental impact.
  • Understand data center infrastructure and the design choices that affect energy efficiency.
  • Understand AI/ML system fundamentals, how they run in data centers, and control knows to adapt their energy usage.

Course Prerequisites

  • A basic background in linear algebra, power systems, and distributed systems is expected. The course provides a comprehensive introduction to AI and machine learning systems, with an emphasis on sustainable computing practices.

Who Should Participate

This course is designed for power engineers, operational engineers, professionals from AI and power companies, leadership teams, and anyone interested in the interaction between artificial intelligence and power systems. The course is ideally suited for professionals in the electric grid or AI industries who seek to understand the latest advancements in AI, machine learning, and sustainable computing, and how these innovations will shape their work. 

Some sample job titles of individuals who would be interested in this content include:

  • Power Systems Engineer
  • Smart Grid Analyst
  • Energy Systems Analyst
  • Grid Modernization Specialist
  • Data Scientist (in the Energy Sector)
  • AI Engineer (in Renewable Energy)
  • Renewable Energy Systems Developer
  • Sustainability Consultant for Energy
  • Electrical Engineering Manager
  • Operations Manager in a Utility Company
  • Data Center Energy Efficiency Consultant
  • Machine Learning Engineer (in Power Sector)
  • Energy Market Analyst
  • Cyber-Physical Systems Engineer
  • Product Manager for Smart Energy Solutions
  • Device vendors for data centers
  • Data center operators
  • Machine learning infrastructure developers

Harvard University welcomes individuals with disabilities to participate in its programs and activities. If you would like to request accommodations or have questions about the physical access provided, please contact ProfEd@seas.harvard.edu in advance of your participation. Requests for American Sign Language interpreters and/or CART providers should be made at least two weeks in advance, if possible. Please note that the University will make every effort to secure services, but that services are subject to availability. 

For more information, contact SEAS Professional Education at ProfEd@seas.harvard.edu.