Data Centers, AI Loads, and Power Density: Electrical Engineering Challenges in the AI Era
 

Course Description:

The course emphasizes practical application of the National Electrical Code (NEC), relevant IEEE standards such as IEEE 519, and accepted industry best practices, while also addressing operational safety under NFPA 70E. By integrating design, protection, cooling, utility, and lifecycle considerations, the course prepares Professional Engineers and Licensed Electricians to safely design, evaluate, operate, and maintain electrical systems capable of supporting the next generation of AI-driven data centers.

Learning Objectives:

1. Explain how artificial intelligence (AI), machine learning, and high-performance computing (HPC) workloads fundamentally differ from traditional data center loads in terms of power density, duty cycle, and electrical behavior.
2. Evaluate power density trends and continuous-load characteristics of AI data centers and apply National Electrical Code (NEC) requirements to feeder, busway, transformer, and overcurrent device sizing.
3. Analyze the electrical impacts of synchronized AI load behavior, including peak demand, ramp rates, voltage stability, and implications for utility interconnection.
4. Identify and assess nonlinear load characteristics of GPU-based computing systems and their effects on harmonics, neutral conductors, transformers, and overall power quality using IEEE 519 guidance.
5. Describe and compare electrical distribution architectures used in high-density AI data centers, including busway systems, zoned distribution, and redundant power paths.
6. Apply redundancy and reliability concepts (N, N+1, 2N) to mission-critical AI data centers, considering continuous operation, scalability, and maintainability.
7. Assess the effects of increased fault current on protection systems, including short-circuit ratings, selective coordination, arc-flash hazards, and worker safety requirements under NEC and NFPA 70E.
8. Evaluate grounding and bonding requirements for high-density electrical systems and integrated cooling infrastructure to ensure effective fault clearing and personnel safety.
9. Explain how cooling strategies—including air, liquid-assisted, and immersion cooling—affect electrical system design, auxiliary loads, redundancy, and operational safety.
10. Analyze utility coordination requirements for large AI data centers, including peak demand planning, phased load deployment, demand management, and grid interaction considerations.
11. Describe operational, maintenance, and lifecycle management challenges associated with continuous, high-duty AI data center electrical systems, including condition-based monitoring and change management.
12. Apply professional engineering judgment to evolving AI data center technologies where codes and standards may lag, balancing safety, reliability, scalability, and ethical responsibility.