Data centers in colleges and universities are crucial for supporting the extensive technological infrastructure required for modern education and research. These centers house critical servers and storage systems that manage vast amounts of data, ensuring reliable access to academic resources, administrative applications, and communication networks. They enable the secure storage and processing of sensitive information, including student records, faculty research, and institutional data.
Moreover, data centers facilitate advanced research by providing the computational power needed for data-intensive studies in fields like bioinformatics, climate science, and artificial intelligence. They support virtual learning environments and online course management systems, essential for the increasingly prevalent hybrid and online education models. Efficient data centers also contribute to campus sustainability goals by optimizing energy use through modern, eco-friendly technologies.
Additionally, robust data center infrastructure enhances the university’s ability to attract top-tier faculty and students by demonstrating a commitment to cutting-edge technology and resources. They also play a vital role in disaster recovery and business continuity, ensuring that educational and administrative functions can resume quickly after disruptions. Overall, data centers are integral to the academic mission, operational efficiency, and strategic growth of colleges and universities.
We have followed development of the technical standards that govern the success of these “installations” since 1993; sometimes nudging technical committees — NFPA, IEEE, ASHRAE, BICSI and UL. The topic is vast and runs fast so today we will review, and perhaps respond to, the public consultations that are posted on a near-daily basis. Use the login credentials at the upper right of our home page.
The RELLIS Data and Research Center project at Texas A&M’s RELLIS Campus, a privately developed facility with about 10,000 SF of dedicated data center space for high-performance computing, is currently in limbo. Construction began over a year ago, with Phase I (a 45,000 SF two-story building) underway as of late 2025. However, the developer, RELLIS Campus Data and Research Center LLC, filed for Chapter 11 bankruptcy in November 2025, raising uncertainties about completion and future progress. No recent official updates from Texas A&M indicate resumption or cancellation.
November 11, 2025 Update:
The project, located on the Texas A&M University System’s Rellis Campus in Bryan (Brazos County), has faced significant delays. Originally slated to begin construction by November 2021, it was pushed back due to the 2021 Winter Storm Uri. In November 2023, construction was announced to start in 2024, with an expected opening in Q3 2024 (July–September). However, no sources confirm completion or operations.Recent developments include:
February 2025: Bryan approved a reinvestment zone on the 25-acre site to attract the data center, with ongoing negotiations.
October 2025: Officials clarified no formal plans have been submitted for the site, despite zoning approvals for potential development.
The project’s official site (rellisdrc.com) states “Site will be available soon,” indicating it’s still under preparation. It’s designed as a 225,000 sq ft Tier III facility with colocation, cloud services, and educational spaces for workforce training.
The RELLIS Data and Research Center will be a public – private development with Texas A&M University. The data center will be built on the new RELLIS Campus located in College Station, Texas. It will offer cloud storage and outstanding managed services. The RELLIS Academy and Research Lab offers the ability for Texas A&M University to give real world data center experience to both students and faculty.
Introduction. [Abstract]. The rapid growth of data centers, with their enormous energy and water demands, necessitates targeted policy interventions to mitigate environmental impacts and protect local communities. To address these issues, states with existing data center tax breaks should adopt sustainable growth policies for data centers, mandating energy audits, strict performance standards, and renewable energy integration, while also requiring transparency in energy usage reporting. “Renewable energy additionality” clauses should ensure data centers contribute to new renewable capacity rather than relying on existing resources. If these measures prove insufficient, states should consider repealing tax breaks to slow unsustainable data center growth. States without tax breaks should avoid such incentives altogether while simultaneously implementing mandatory reporting requirements to hold data centers accountable for their environmental impact. Broader measures should include protecting local tax revenues for schools, regulating utility rate hikes to prevent cost-shifting to consumers, and aligning data center energy demands with state climate goals to avoid prolonging reliance on fossil fuels.
Anglo-American English must remain the standard language of the AI zeitgeist because it dominates the vast training data fueling large language models—often ~90% English, heavily skewed toward American variants due to Silicon Valley’s influence, internet content prevalence, and U.S. tech leadership.
This ensures peak performance, nuance, and reliability in AI outputs. Their global status as lingua francas in science, programming, and digital culture sustains innovation momentum, cross-border collaboration, and accessibility, preventing fragmentation while the field advances.
A Selection of Electrotechnical Terms Evolved from the AI Zeitgeist
Relevant to Our Work for Educational Settlement Safety and Sustainability
#
Term
Definition
1
Artificial Neural Network (ANN)
A computational model mimicking biological neurons, used in power systems for load forecasting and fault classification by learning patterns from electrical data.
2
Deep Neural Network (DNN)
Multi-layered ANN for complex tasks like state estimation in grids, enabling deeper analysis of electrical signals for predictive maintenance.
3
Convolutional Neural Network (CNN)
A DNN specialized for processing grid-like data, applied in image-based fault detection on power lines or substations using drone visuals.
4
Recurrent Neural Network (RNN)
Neural network handling sequential data, evolved for time-series forecasting in energy demand and renewable integration in electrical networks.
5
Long Short-Term Memory (LSTM)
An RNN variant that remembers long-term dependencies, used for accurate wind/solar power prediction in dynamic electrical systems.
6
Graph Neural Network (GNN)
Processes graph-structured data like power grids, optimizing flow analysis and topology estimation in transmission networks.
7
Generative Adversarial Network (GAN)
Dual-network system generating synthetic data, applied to simulate electrical scenarios for training models in scarce-data power environments.
8
Reinforcement Learning (RL)
Learning through trial-and-error rewards, used for adaptive control in grid optimization and emergency load shedding.
9
Deep Reinforcement Learning (DRL)
RL combined with DNNs, enabling autonomous decision-making in real-time power system stability and demand response.
10
Smart Grid
AI-enhanced electrical distribution network that uses real-time data for self-healing, load balancing, and renewable integration.
11
Digital Twin
Virtual AI-simulated replica of electrical infrastructure, allowing scenario testing for predictive fault avoidance in power plants.
12
Edge AI
Decentralized AI processing at network edges, enabling low-latency control in IoT-enabled electrical devices and microgrids.
13
Neuromorphic Computing
Brain-inspired hardware chips for efficient AI, reducing power consumption in electrotechnical applications like sensor networks.
14
Tensor Processing Unit (TPU)
Specialized ASIC for AI workloads, accelerating matrix operations in electrical system simulations and optimization.
15
Predictive Maintenance
AI-driven monitoring of electrical assets (e.g., transformers) to forecast failures using sensor data and ML algorithms.
16
Optimal Power Flow (OPF)
AI-optimized calculation of efficient power distribution, minimizing losses in transmission lines via ML approximations.
17
Microgrid
Localized AI-managed grid, enabling autonomous operation with renewables, using RL for energy balancing.
18
Phasor Measurement Unit (PMU)
High-speed sensor providing synchronized data for AI-based state estimation and oscillation detection in power systems.
19
Supervisory Control and Data Acquisition (SCADA)
Traditional system evolved with AI for enhanced monitoring, anomaly detection, and automated control in electrical utilities.
20
High-Impedance Fault (HIF) Detection
AI techniques like SVM or CNN to identify subtle faults in distribution lines, improving safety and reliability.
21
Load Forecasting
ML models predicting electricity demand, incorporating weather and usage patterns for grid planning.
22
Demand Response
AI-optimized strategy adjusting consumer loads in real-time, using RL to balance supply in volatile renewable-heavy systems.
23
Energy Management System (EMS)
AI-integrated platform for overseeing generation, transmission, and distribution, enhancing efficiency with predictive analytics.
24
Power Electronic Converter (PEC)
Devices like inverters controlled by AI for fault-tolerant operation in renewables and EVs.
25
Composite Load Model (CLM)
AI-tuned aggregated model of electrical loads, using ML for dynamic simulation in stability studies.
The Center provides comprehensive healthcare services to students. Located on the Logan campus, the clinic offers a range of medical services including general health check-ups, vaccinations, mental health support, and chronic disease management. Staffed by experienced physicians, nurse practitioners, and support staff, the clinic aims to address both physical and mental health needs. Students can access acute care for illnesses and injuries, preventive care, women’s health services, and counseling.
The clinic also provides lab services, prescriptions, and referrals to specialists when needed. With a focus on promoting wellness and healthy lifestyles, the USU Student Health Clinic ensures that students receive quality care in a supportive environment, contributing to their overall well-being and academic success. The clinic operates on an appointment basis, with some walk-in availability, and is committed to maintaining confidentiality and respect for all students.
Because electrotechnology changes continually, definitions (vocabulary) in its best practice literature changes continually; not unlike any language on earth that adapts to the moment and place.
The changes reflect changes in technology or changes in how the technology works in practice; even how the manufacturers create adaptations to field conditions by combining functions. Any smart electrical component has a digital language embedded in it, for example.
Consider the 2023 National Electrical Code. Apart from many others the NEC will contain a major change to Article 100 (Definitions); the subject of elevated debate over the past three years.
When we refer “language” we must distinguish between formal language, informal language, colloquial language and dialect which may differ the language spoken, language written at the office and language used on the job site. “Terms of art”
Are these terms (or, “terms of art”) best understood in context (upstream articles in Chapters 4 through 8) — or should they be adjudicated by the 14 Principals of Code Making Panel 1? The answer will arrive in the fullness of time. Many changes to the National Electrical Code require more than one cycle to stabilize.
Code Making Panel 1 has always been the heaviest of all NEC panels. As explained n our ABOUT, the University of Michigan held a vote in CMP-1 for 20+ years (11 revision cycles) before moving to the healthcare facilities committee for the IEEE Education & Healthcare Facilities Committee. Standards Michigan continues its involvement on behalf of the US education facility industry — the second largest building construction market. There is no other pure user-interest voice on any technical committee; although in some cases consulting companies are retained for special purposes.
To serve the purpose of making NFPA 70 more “useable” we respect the Standards Council decision to make this change if it contributes to the viability of the NFPA business model. We get to say this because no other trade association comes close to having as enduring and as strong a voice: NFPA stands above all other US-based SDO’s in fairness and consideration of its constituency. The electrical safety community in the United States is a mighty tough crowd.
If the change does not work, or work well enough, nothing should prohibit reversing the trend toward “re-centralizing” — or “de-centralizing” the definitions.
Public comment on the First Draft of the 2026 Edition will be received until August 28, 2024.
Technical Committees meet during the last half of October to respond to public comment on the First Draft of the 2026 National Electrical Code.
New update alert! The 2022 update to the Trademark Assignment Dataset is now available online. Find 1.29 million trademark assignments, involving 2.28 million unique trademark properties issued by the USPTO between March 1952 and January 2023: https://t.co/njrDAbSpwBpic.twitter.com/GkAXrHoQ9T
